Elkind, Emese; Tun, Aung Tin; Radcliffe, Olivia; Connolly, Laura; Davison, Colleen; Purkey, Eva; Mousavi, Parvin; Fichtinger, Gabor; Thornton, Kanchana
2024 Canadian Conference on Global Health, Canadian Association for Global Health, 2024.
@conference{Elkind2024b,
title = {Enhancing healthcare access by developing low-cost 3D printed prosthetics along the Thai-Myanmar border},
author = {Emese Elkind and Aung Tin Tun and Olivia Radcliffe and Laura Connolly and Colleen Davison and Eva Purkey and Parvin Mousavi and Gabor Fichtinger and Kanchana Thornton
},
url = {https://labs.cs.queensu.ca/perklab/wp-content/uploads/sites/3/2024/10/EElkind_CCGH2024.pdf},
year = {2024},
date = {2024-10-25},
urldate = {2024-10-25},
booktitle = {2024 Canadian Conference on Global Health},
publisher = {Canadian Association for Global Health},
abstract = {Background/Objective
Inadequacies in the Burmese healthcare system, heightened by the 2021 military coup of the civil war in Myanmar and the COVID-19 pandemic, have driven thousands of refugees to Thailand seeking medical aid. Without immigration status, these refugees, especially those who have experienced limb loss, are challenged by the inability to receive healthcare. Burma Children Medical Fund (BCMF, www.burmachildren.com) based in Mae Sot, Tak, Thailand focuses on funding underserved Burmese communities’ medical treatment and providing support services.
Prosthetics in lower-income countries are usually passive, therefore, patients cannot fully perform their daily functions, impacting their abilities to work and affecting family caretakers. BCMF aims to make body-powered prosthetics that work best in low-resource settings using open-source designs, which only allow for fixed hand positions. The usage of prosthetic arms depends heavily on their functionality and comfort. Patients are more likely to consistently use prosthetics if it aids them in returning to normalcy and reducing family burdens. My objective is to design an interchangeable hand to enable critical rotational movements.
Methodology
The BCMF prosthetics project makes custom-fitted, low-cost, 3D-printed prostheses. BCMF uses open-source prosthetic models such as the Kwawu Arm 2.0, which provides an OpenSCAD (openscad.org) file for adjusting the model to the recipient's measurements. To maintain BCMF’s workflow, the interchangeable wrist model was created using the 3D design software, Autodesk Fusion 360, and designs from NIOP Q-C v1 and v2 Quick-Connect Wrist. The wrist was merged onto the Kwawu Arm, printed, assembled, and tested. This is an iterative process where patient feedback ensures the prosthetics cater to the diverse needs of the recipients.
Results
Since the launch of the prosthetics project in 2019, BCMF has provided 3D-printed prosthetics to 76 patients. The interchangeable hand provides a solution to many patients' everyday activities and can rotate the hand 360 degrees.
Conclusions
This project provides a low-cost solution to healthcare challenges in the context of poly-crisis experienced in Myanmar, enhancing the resilience and adaptability of affected refugee communities.
Relevance to Sub-Theme
This presentation aligns with sub-theme 2 by developing and testing methods to improve healthcare access and quality in areas affected by war, migration, poverty, and racial disparities.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Background/Objective
Inadequacies in the Burmese healthcare system, heightened by the 2021 military coup of the civil war in Myanmar and the COVID-19 pandemic, have driven thousands of refugees to Thailand seeking medical aid. Without immigration status, these refugees, especially those who have experienced limb loss, are challenged by the inability to receive healthcare. Burma Children Medical Fund (BCMF, www.burmachildren.com) based in Mae Sot, Tak, Thailand focuses on funding underserved Burmese communities’ medical treatment and providing support services.
Prosthetics in lower-income countries are usually passive, therefore, patients cannot fully perform their daily functions, impacting their abilities to work and affecting family caretakers. BCMF aims to make body-powered prosthetics that work best in low-resource settings using open-source designs, which only allow for fixed hand positions. The usage of prosthetic arms depends heavily on their functionality and comfort. Patients are more likely to consistently use prosthetics if it aids them in returning to normalcy and reducing family burdens. My objective is to design an interchangeable hand to enable critical rotational movements.
Methodology
The BCMF prosthetics project makes custom-fitted, low-cost, 3D-printed prostheses. BCMF uses open-source prosthetic models such as the Kwawu Arm 2.0, which provides an OpenSCAD (openscad.org) file for adjusting the model to the recipient's measurements. To maintain BCMF’s workflow, the interchangeable wrist model was created using the 3D design software, Autodesk Fusion 360, and designs from NIOP Q-C v1 and v2 Quick-Connect Wrist. The wrist was merged onto the Kwawu Arm, printed, assembled, and tested. This is an iterative process where patient feedback ensures the prosthetics cater to the diverse needs of the recipients.
Results
Since the launch of the prosthetics project in 2019, BCMF has provided 3D-printed prosthetics to 76 patients. The interchangeable hand provides a solution to many patients' everyday activities and can rotate the hand 360 degrees.
Conclusions
This project provides a low-cost solution to healthcare challenges in the context of poly-crisis experienced in Myanmar, enhancing the resilience and adaptability of affected refugee communities.
Relevance to Sub-Theme
This presentation aligns with sub-theme 2 by developing and testing methods to improve healthcare access and quality in areas affected by war, migration, poverty, and racial disparities.
Inadequacies in the Burmese healthcare system, heightened by the 2021 military coup of the civil war in Myanmar and the COVID-19 pandemic, have driven thousands of refugees to Thailand seeking medical aid. Without immigration status, these refugees, especially those who have experienced limb loss, are challenged by the inability to receive healthcare. Burma Children Medical Fund (BCMF, www.burmachildren.com) based in Mae Sot, Tak, Thailand focuses on funding underserved Burmese communities’ medical treatment and providing support services.
Prosthetics in lower-income countries are usually passive, therefore, patients cannot fully perform their daily functions, impacting their abilities to work and affecting family caretakers. BCMF aims to make body-powered prosthetics that work best in low-resource settings using open-source designs, which only allow for fixed hand positions. The usage of prosthetic arms depends heavily on their functionality and comfort. Patients are more likely to consistently use prosthetics if it aids them in returning to normalcy and reducing family burdens. My objective is to design an interchangeable hand to enable critical rotational movements.
Methodology
The BCMF prosthetics project makes custom-fitted, low-cost, 3D-printed prostheses. BCMF uses open-source prosthetic models such as the Kwawu Arm 2.0, which provides an OpenSCAD (openscad.org) file for adjusting the model to the recipient's measurements. To maintain BCMF’s workflow, the interchangeable wrist model was created using the 3D design software, Autodesk Fusion 360, and designs from NIOP Q-C v1 and v2 Quick-Connect Wrist. The wrist was merged onto the Kwawu Arm, printed, assembled, and tested. This is an iterative process where patient feedback ensures the prosthetics cater to the diverse needs of the recipients.
Results
Since the launch of the prosthetics project in 2019, BCMF has provided 3D-printed prosthetics to 76 patients. The interchangeable hand provides a solution to many patients' everyday activities and can rotate the hand 360 degrees.
Conclusions
This project provides a low-cost solution to healthcare challenges in the context of poly-crisis experienced in Myanmar, enhancing the resilience and adaptability of affected refugee communities.
Relevance to Sub-Theme
This presentation aligns with sub-theme 2 by developing and testing methods to improve healthcare access and quality in areas affected by war, migration, poverty, and racial disparities.
Warren, Jade; Jamzad, Amoon; Jamaspishvili, Tamara; Iseman, Rachael; Syeda, Ayesha; Kaufmann, Martin; Rudan, John; Fichtinger, Gabor; Berman, David M.; Mousavi, Parvin
Towards Improving Surgical Margins in Tumour Resection Using Mass Spectrometry Imaging Proceedings
2024, ISBN: 979-8-3503-7163-5.
@proceedings{10667088,
title = {Towards Improving Surgical Margins in Tumour Resection Using Mass Spectrometry Imaging},
author = {Jade Warren and Amoon Jamzad and Tamara Jamaspishvili and Rachael Iseman and Ayesha Syeda and Martin Kaufmann and John Rudan and Gabor Fichtinger and David M. Berman and Parvin Mousavi},
doi = {10.1109/CCECE59415.2024.10667088},
isbn = {979-8-3503-7163-5},
year = {2024},
date = {2024-09-21},
urldate = {2024-09-21},
abstract = {Successful cancer resection is limited by the inability to differentiate between cancer and normal tissue intraoperatively. Desorption electrospray ionization mass spectrometry imaging (DESI-MSI) is an emerging and powerful analytical technique that offers a rapid and low cost approach for assessing surgical margins by generating detailed metabolic profiles. However, exploiting this data for tissue characterization based on molecular signals requires machine learning methods to handle its complexity. In this work, we utilize machine learning models for the characterization of tissue using DESI-MSI data obtained from prostate tissue samples. We use ViPRE, a novel open-source software, to annotate a large DESI-MSI dataset. We explore various machine learning models and train test schemes for cancer classification. Cross-validation of our models result in high balanced accuracy, sensitivity and specificity for cancer classification. Furthermore, we simulate the prospective application of perioperative tissue characterization, generating a qualitative visual prediction for whole slides that match pathology annotations. Finally, the application of linear transformation and classification algorithms on DESI-MSI data effectively distinguished between the molecular profiles associated with different cancer grades. Our findings highlight the promise of combining machine learning with large DESI-MSI datasets for tissue characterization, thereby improving surgical margin precision.},
keywords = {},
pubstate = {published},
tppubtype = {proceedings}
}
Successful cancer resection is limited by the inability to differentiate between cancer and normal tissue intraoperatively. Desorption electrospray ionization mass spectrometry imaging (DESI-MSI) is an emerging and powerful analytical technique that offers a rapid and low cost approach for assessing surgical margins by generating detailed metabolic profiles. However, exploiting this data for tissue characterization based on molecular signals requires machine learning methods to handle its complexity. In this work, we utilize machine learning models for the characterization of tissue using DESI-MSI data obtained from prostate tissue samples. We use ViPRE, a novel open-source software, to annotate a large DESI-MSI dataset. We explore various machine learning models and train test schemes for cancer classification. Cross-validation of our models result in high balanced accuracy, sensitivity and specificity for cancer classification. Furthermore, we simulate the prospective application of perioperative tissue characterization, generating a qualitative visual prediction for whole slides that match pathology annotations. Finally, the application of linear transformation and classification algorithms on DESI-MSI data effectively distinguished between the molecular profiles associated with different cancer grades. Our findings highlight the promise of combining machine learning with large DESI-MSI datasets for tissue characterization, thereby improving surgical margin precision.
Kim, Andrew S.; Yeung, Chris; Szabo, Robert; Sunderland, Kyle; Hisey, Rebecca; Morton, David; Kikinis, Ron; Diao, Babacar; Mousavi, Parvin; Ungi, Tamas; Fichtinger, Gabor
SPIE, 2024.
@proceedings{Kim2024,
title = {Percutaneous nephrostomy needle guidance using real-time 3D anatomical visualization with live ultrasound segmentation},
author = {Andrew S. Kim and Chris Yeung and Robert Szabo and Kyle Sunderland and Rebecca Hisey and David Morton and Ron Kikinis and Babacar Diao and Parvin Mousavi and Tamas Ungi and Gabor Fichtinger},
editor = {Maryam E. Rettmann and Jeffrey H. Siewerdsen},
doi = {10.1117/12.3006533},
year = {2024},
date = {2024-03-29},
urldate = {2024-03-29},
publisher = {SPIE},
abstract = {
PURPOSE: Percutaneous nephrostomy is a commonly performed procedure to drain urine to provide relief in patients with hydronephrosis. Conventional percutaneous nephrostomy needle guidance methods can be difficult, expensive, or not portable. We propose an open-source real-time 3D anatomical visualization aid for needle guidance with live ultrasound segmentation and 3D volume reconstruction using free, open-source software. METHODS: Basic hydronephrotic kidney phantoms were created, and recordings of these models were manually segmented and used to train a deep learning model that makes live segmentation predictions to perform live 3D volume reconstruction of the fluid-filled cavity. Participants performed 5 needle insertions with the visualization aid and 5 insertions with ultrasound needle guidance on a kidney phantom in randomized order, and these were recorded. Recordings of the trials were analyzed for needle tip distance to the center of the target calyx, needle insertion time, and success rate. Participants also completed a survey on their experience. RESULTS: Using the visualization aid showed significantly higher accuracy, while needle insertion time and success rate were not statistically significant at our sample size. Participants mostly responded positively to the visualization aid, and 80% found it easier to use than ultrasound needle guidance. CONCLUSION: We found that our visualization aid produced increased accuracy and an overall positive experience. We demonstrated that our system is functional and stable and believe that the workflow with this system can be applied to other procedures. This visualization aid system is effective on phantoms and is ready for translation with clinical data.},
keywords = {},
pubstate = {published},
tppubtype = {proceedings}
}
PURPOSE: Percutaneous nephrostomy is a commonly performed procedure to drain urine to provide relief in patients with hydronephrosis. Conventional percutaneous nephrostomy needle guidance methods can be difficult, expensive, or not portable. We propose an open-source real-time 3D anatomical visualization aid for needle guidance with live ultrasound segmentation and 3D volume reconstruction using free, open-source software. METHODS: Basic hydronephrotic kidney phantoms were created, and recordings of these models were manually segmented and used to train a deep learning model that makes live segmentation predictions to perform live 3D volume reconstruction of the fluid-filled cavity. Participants performed 5 needle insertions with the visualization aid and 5 insertions with ultrasound needle guidance on a kidney phantom in randomized order, and these were recorded. Recordings of the trials were analyzed for needle tip distance to the center of the target calyx, needle insertion time, and success rate. Participants also completed a survey on their experience. RESULTS: Using the visualization aid showed significantly higher accuracy, while needle insertion time and success rate were not statistically significant at our sample size. Participants mostly responded positively to the visualization aid, and 80% found it easier to use than ultrasound needle guidance. CONCLUSION: We found that our visualization aid produced increased accuracy and an overall positive experience. We demonstrated that our system is functional and stable and believe that the workflow with this system can be applied to other procedures. This visualization aid system is effective on phantoms and is ready for translation with clinical data.
Klosa, Elizabeth; Levendovics, Renáta; Takács, Kristóf; Fichtinger, Gabor; Haidegger, Tamás
Exploring heart rate variability metrics for stress assessment in robot-assisted surgery training Conference
2024.
@conference{nokey,
title = {Exploring heart rate variability metrics for stress assessment in robot-assisted surgery training},
author = {Elizabeth Klosa and Renáta Levendovics and Kristóf Takács and Gabor Fichtinger and Tamás Haidegger},
url = {https://www.imno.ca/sites/default/files/ImNO2024-Proceedings.pdf},
year = {2024},
date = {2024-03-20},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Elkind, Emese; Barr, Keiran; Barr, Colton; Moga, Kristof; Garamvolgy, Tivadar; Haidegger, Tamas; Ungi, Tamas; Fichtinger, Gabor
Modifying Radix Lenses to Survive Low-Cost Sterilization: An Exploratory Study Conference
Imaging Network of Ontario (ImNO) Symposium, Imaging Network of Ontario (ImNO) Symposium, 2024.
@conference{Elkind2024,
title = {Modifying Radix Lenses to Survive Low-Cost Sterilization: An Exploratory Study},
author = {Emese Elkind and Keiran Barr and Colton Barr and Kristof Moga and Tivadar Garamvolgy and Tamas Haidegger and Tamas Ungi and Gabor Fichtinger},
url = {https://labs.cs.queensu.ca/perklab/wp-content/uploads/sites/3/2024/10/EmeseElkindImNO2024-2.docx},
year = {2024},
date = {2024-03-19},
urldate = {2024-03-19},
booktitle = {Imaging Network of Ontario (ImNO) Symposium},
publisher = {Imaging Network of Ontario (ImNO) Symposium},
abstract = {INTRODUCTION: A major challenge with deploying infrared camera-tracked surgical navigation solutions, such as NousNav [1], in low-resource settings is the high cost and unavailability of disposable retroreflective infrared markers. Developing an accessible method to reuse and sterilize retroreflective markers could lead to significant increase in the uptake of this technology. As none of the known infrared markers can endure standard autoclaving and most places do not have access to gas sterilization, attention is focused on cold liquid sterilisation methods commonly used in laparoscopy and other optical tools that cannot be sterilized in a conventional autoclave.
METHODS: We propose to modify NDI Radix™ Lens [1], single-use retroreflective spherical marker manufactured by Northern Digital, Waterloo, Canada. Radix lenses are uniquely promising candidates for liquid sterilization given their smooth, spherical surface. This quality also makes them easier to clean perioperatively compared to other retroreflective infrared marker designs. Initial experiments show that liquid sterilization agents degrade the marker’s retroreflective gold coating (Fig. 1). Hence the objective of this project is to develop a method to protect the Radix Lenses with a layer of coating material that does not allow the sanitizing agent to degrade the coating to enable the lens to survive multiple sanitation cycles while retaining sufficient tracking accuracy. We employed two cold liquid sterilisation agents, household bleach which is a common ingredient of liquid sterilisation solutions and Sekusept™ Aktiv (Ecolab, Saint Paul, MN, USA), which is widely known for sterilizing laparoscopy instruments. Store-bought nail polish and Zink-Alu Spray were used to coat the lenses. Data were obtained by recording five tests each with five rounds of sterilization, each tested with six trials, for a total of 150 recordings. The five tests were as follows: 1) Radix lens coated with nail polish and bleached, 2) uncoated and bleached, 3) coated with nail polish and sanitised, 4) uncoated and sanitised, and 5) coated with Zink-Alu Spray and sanitised. To assess the impact of the sterilization on the lens’s fiducial localization error, two metal marker frames equipped with four sockets designed for the Radix lenses were used. The reference marker frame was secured to a flat table while the other marker frame moved along a fixed path on the table. The position and orientation of the marker clusters were streamed into 3D Slicer using the Public Library for Ultrasound Toolkit (PLUS). A plane was then fit to the recorded marker poses in 3D Slicer using Iterative Closest Point and the marker registration error was computed. Distance from the camera, angle of view, and distance from the edges of the field of view were held constant.
RESULTS: With each round of sterilization, the error of coated lenses was lower than the unprotected lenses, and the error showed a slightly increasing trend (Fig. 2). The lenses appeared fainter in the tracking software the lenses appeared fainter while all lenses remained trackable and visible despite the significant removal of reflective coating.
When reflective coating was fully rubbed off the lenses, the tracking software could still localize the markers; however, the lenses did appear much fainter in the tracking software. We observed that the reflective coating rubs off the lens in routine handling, and recoating with Zink-Alu spray can partially restore marker visibility. Using protective nail polish coating prevented the reflective coating from rubbing off altogether.
CONCLUSIONS: This exploratory study represents a promising step toward achieving low-cost sterilization of retroreflective infrared markers. Studies with the NousNav system need to be undertaken to measure the extent of degradation in tracking accuracy is tolerable as a side effect of marker sterilization. Before using coated Radix lenses on human subjects, it must be verified that the protective coating (common nail polish in our study) is fully biocompatible and remains undamaged by the cold sterilization agent (Sekusept™ Aktiv in our study.)
REFERENCES: [1] NousNav: A low-cost neuronavigation system for deployment in lower-resource settings, International Journal of Computer Assisted Radiology and Surgery, 2022 Sep;17(9):1745-1750. [2] NDI Radix™ Lens (https://www.ndigital.com/optical-measurement-technology/radix-lens/) },
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
INTRODUCTION: A major challenge with deploying infrared camera-tracked surgical navigation solutions, such as NousNav [1], in low-resource settings is the high cost and unavailability of disposable retroreflective infrared markers. Developing an accessible method to reuse and sterilize retroreflective markers could lead to significant increase in the uptake of this technology. As none of the known infrared markers can endure standard autoclaving and most places do not have access to gas sterilization, attention is focused on cold liquid sterilisation methods commonly used in laparoscopy and other optical tools that cannot be sterilized in a conventional autoclave.
METHODS: We propose to modify NDI Radix™ Lens [1], single-use retroreflective spherical marker manufactured by Northern Digital, Waterloo, Canada. Radix lenses are uniquely promising candidates for liquid sterilization given their smooth, spherical surface. This quality also makes them easier to clean perioperatively compared to other retroreflective infrared marker designs. Initial experiments show that liquid sterilization agents degrade the marker’s retroreflective gold coating (Fig. 1). Hence the objective of this project is to develop a method to protect the Radix Lenses with a layer of coating material that does not allow the sanitizing agent to degrade the coating to enable the lens to survive multiple sanitation cycles while retaining sufficient tracking accuracy. We employed two cold liquid sterilisation agents, household bleach which is a common ingredient of liquid sterilisation solutions and Sekusept™ Aktiv (Ecolab, Saint Paul, MN, USA), which is widely known for sterilizing laparoscopy instruments. Store-bought nail polish and Zink-Alu Spray were used to coat the lenses. Data were obtained by recording five tests each with five rounds of sterilization, each tested with six trials, for a total of 150 recordings. The five tests were as follows: 1) Radix lens coated with nail polish and bleached, 2) uncoated and bleached, 3) coated with nail polish and sanitised, 4) uncoated and sanitised, and 5) coated with Zink-Alu Spray and sanitised. To assess the impact of the sterilization on the lens’s fiducial localization error, two metal marker frames equipped with four sockets designed for the Radix lenses were used. The reference marker frame was secured to a flat table while the other marker frame moved along a fixed path on the table. The position and orientation of the marker clusters were streamed into 3D Slicer using the Public Library for Ultrasound Toolkit (PLUS). A plane was then fit to the recorded marker poses in 3D Slicer using Iterative Closest Point and the marker registration error was computed. Distance from the camera, angle of view, and distance from the edges of the field of view were held constant.
RESULTS: With each round of sterilization, the error of coated lenses was lower than the unprotected lenses, and the error showed a slightly increasing trend (Fig. 2). The lenses appeared fainter in the tracking software the lenses appeared fainter while all lenses remained trackable and visible despite the significant removal of reflective coating.
When reflective coating was fully rubbed off the lenses, the tracking software could still localize the markers; however, the lenses did appear much fainter in the tracking software. We observed that the reflective coating rubs off the lens in routine handling, and recoating with Zink-Alu spray can partially restore marker visibility. Using protective nail polish coating prevented the reflective coating from rubbing off altogether.
CONCLUSIONS: This exploratory study represents a promising step toward achieving low-cost sterilization of retroreflective infrared markers. Studies with the NousNav system need to be undertaken to measure the extent of degradation in tracking accuracy is tolerable as a side effect of marker sterilization. Before using coated Radix lenses on human subjects, it must be verified that the protective coating (common nail polish in our study) is fully biocompatible and remains undamaged by the cold sterilization agent (Sekusept™ Aktiv in our study.)
REFERENCES: [1] NousNav: A low-cost neuronavigation system for deployment in lower-resource settings, International Journal of Computer Assisted Radiology and Surgery, 2022 Sep;17(9):1745-1750. [2] NDI Radix™ Lens (https://www.ndigital.com/optical-measurement-technology/radix-lens/)
METHODS: We propose to modify NDI Radix™ Lens [1], single-use retroreflective spherical marker manufactured by Northern Digital, Waterloo, Canada. Radix lenses are uniquely promising candidates for liquid sterilization given their smooth, spherical surface. This quality also makes them easier to clean perioperatively compared to other retroreflective infrared marker designs. Initial experiments show that liquid sterilization agents degrade the marker’s retroreflective gold coating (Fig. 1). Hence the objective of this project is to develop a method to protect the Radix Lenses with a layer of coating material that does not allow the sanitizing agent to degrade the coating to enable the lens to survive multiple sanitation cycles while retaining sufficient tracking accuracy. We employed two cold liquid sterilisation agents, household bleach which is a common ingredient of liquid sterilisation solutions and Sekusept™ Aktiv (Ecolab, Saint Paul, MN, USA), which is widely known for sterilizing laparoscopy instruments. Store-bought nail polish and Zink-Alu Spray were used to coat the lenses. Data were obtained by recording five tests each with five rounds of sterilization, each tested with six trials, for a total of 150 recordings. The five tests were as follows: 1) Radix lens coated with nail polish and bleached, 2) uncoated and bleached, 3) coated with nail polish and sanitised, 4) uncoated and sanitised, and 5) coated with Zink-Alu Spray and sanitised. To assess the impact of the sterilization on the lens’s fiducial localization error, two metal marker frames equipped with four sockets designed for the Radix lenses were used. The reference marker frame was secured to a flat table while the other marker frame moved along a fixed path on the table. The position and orientation of the marker clusters were streamed into 3D Slicer using the Public Library for Ultrasound Toolkit (PLUS). A plane was then fit to the recorded marker poses in 3D Slicer using Iterative Closest Point and the marker registration error was computed. Distance from the camera, angle of view, and distance from the edges of the field of view were held constant.
RESULTS: With each round of sterilization, the error of coated lenses was lower than the unprotected lenses, and the error showed a slightly increasing trend (Fig. 2). The lenses appeared fainter in the tracking software the lenses appeared fainter while all lenses remained trackable and visible despite the significant removal of reflective coating.
When reflective coating was fully rubbed off the lenses, the tracking software could still localize the markers; however, the lenses did appear much fainter in the tracking software. We observed that the reflective coating rubs off the lens in routine handling, and recoating with Zink-Alu spray can partially restore marker visibility. Using protective nail polish coating prevented the reflective coating from rubbing off altogether.
CONCLUSIONS: This exploratory study represents a promising step toward achieving low-cost sterilization of retroreflective infrared markers. Studies with the NousNav system need to be undertaken to measure the extent of degradation in tracking accuracy is tolerable as a side effect of marker sterilization. Before using coated Radix lenses on human subjects, it must be verified that the protective coating (common nail polish in our study) is fully biocompatible and remains undamaged by the cold sterilization agent (Sekusept™ Aktiv in our study.)
REFERENCES: [1] NousNav: A low-cost neuronavigation system for deployment in lower-resource settings, International Journal of Computer Assisted Radiology and Surgery, 2022 Sep;17(9):1745-1750. [2] NDI Radix™ Lens (https://www.ndigital.com/optical-measurement-technology/radix-lens/)
Hisey, Rebecca; Ndiaye, Fatou Bintou; Sunderland, Kyle; Seck, Idrissa; Mbaye, Moustapha; Keita, Mohammed; Diahame, Mamadou; Kikinis, Ron; Diao, Babacar; Fichtinger, Gabor; Camara, Mamadou
Feasibility of video‐based skill assessment for percutaneous nephrostomy training in Senegal Journal Article
In: Healthcare Technology Letters, 2024.
@article{hisey2024a,
title = {Feasibility of video‐based skill assessment for percutaneous nephrostomy training in Senegal},
author = {Rebecca Hisey and Fatou Bintou Ndiaye and Kyle Sunderland and Idrissa Seck and Moustapha Mbaye and Mohammed Keita and Mamadou Diahame and Ron Kikinis and Babacar Diao and Gabor Fichtinger and Mamadou Camara},
year = {2024},
date = {2024-01-01},
journal = {Healthcare Technology Letters},
abstract = {Percutaneous nephrostomy can be an effective means of preventing irreparable renal damage from obstructive renal disease thereby providing patients with more time to access treatment to remove the source of the blockage. In sub‐Saharan Africa, where there is limited access to treatments such as dialysis and transplantation, a nephrostomy can be life‐saving. Training this procedure in simulation can allow trainees to develop their technical skills without risking patient safety, but still requires an ex‐pert observer to provide performative feedback. In this study, the feasibility of using video as an accessible method to assess skill in simulated percutaneous nephrostomy is evaluated. Six novice urology residents and six expert urologists from Ouakam Military Hospital in Dakar, Senegal performed 4 nephrostomies each using the setup. Motion‐based metrics were computed for each trial from the predicted bounding …},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Percutaneous nephrostomy can be an effective means of preventing irreparable renal damage from obstructive renal disease thereby providing patients with more time to access treatment to remove the source of the blockage. In sub‐Saharan Africa, where there is limited access to treatments such as dialysis and transplantation, a nephrostomy can be life‐saving. Training this procedure in simulation can allow trainees to develop their technical skills without risking patient safety, but still requires an ex‐pert observer to provide performative feedback. In this study, the feasibility of using video as an accessible method to assess skill in simulated percutaneous nephrostomy is evaluated. Six novice urology residents and six expert urologists from Ouakam Military Hospital in Dakar, Senegal performed 4 nephrostomies each using the setup. Motion‐based metrics were computed for each trial from the predicted bounding …
Hisey, Rebecca; Ndiaye, Fatou Bintou; Sunderland, Kyle; Seck, Idrissa; Mbaye, Moustapha; Keita, Mohamed; Diahame, Mamadou; Kikinis, Ron; Diao, Babacar; Fichtinger, Gabor; Camara, Mamadou
Feasibility of video-based skill assessment for percutaneous nephrostomy training in Senegal Journal Article
In: 2024.
@article{hisey2024,
title = {Feasibility of video-based skill assessment for percutaneous nephrostomy training in Senegal},
author = {Rebecca Hisey and Fatou Bintou Ndiaye and Kyle Sunderland and Idrissa Seck and Moustapha Mbaye and Mohamed Keita and Mamadou Diahame and Ron Kikinis and Babacar Diao and Gabor Fichtinger and Mamadou Camara},
year = {2024},
date = {2024-01-01},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Connolly, Laura; Kumar, Aravind S; Mehta, Kapi Ketan; Al-Zogbi, Lidia; Kazanzides, Peter; Mousavi, Parvin; Fichtinger, Gabor; Krieger, Axel; Tokuda, Junichi; Taylor, Russell H; Leonard, Simon; Deguet, Anton
SlicerROS2: A Research and Development Module for Image-Guided Robotic Interventions Journal Article
In: IEEE Transactions on Medical Robotics and Bionics, 2024.
@article{connolly2024,
title = {SlicerROS2: A Research and Development Module for Image-Guided Robotic Interventions},
author = {Laura Connolly and Aravind S Kumar and Kapi Ketan Mehta and Lidia Al-Zogbi and Peter Kazanzides and Parvin Mousavi and Gabor Fichtinger and Axel Krieger and Junichi Tokuda and Russell H Taylor and Simon Leonard and Anton Deguet},
year = {2024},
date = {2024-01-01},
journal = {IEEE Transactions on Medical Robotics and Bionics},
publisher = {IEEE},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Kaufmann, Martin; Vaysse, Pierre-Maxence; Savage, Adele; Kooreman, Loes FS; Janssen, Natasja; Varma, Sonal; Ren, Kevin Yi Mi; Merchant, Shaila; Engel, Cecil Jay; Damink, Steven WM Olde; Smidt, Marjolein L; Shousha, Sami; Chauhan, Hemali; Karali, Evdoxia; Kazanc, Emine; Poulogiannis, George; Fichtinger, Gabor; Tauber, Boglárka; Leff, Daniel R; Pringle, Steven D; Rudan, John F; Heeren, Ron MA; Siegel, Tiffany Porta; Takáts, Zoltán; Balog, Júlia
Testing of rapid evaporative mass spectrometry for histological tissue classification and molecular diagnostics in a multi-site study Journal Article
In: British Journal of Cancer, pp. 1-11, 2024.
@article{kaufmann2024,
title = {Testing of rapid evaporative mass spectrometry for histological tissue classification and molecular diagnostics in a multi-site study},
author = {Martin Kaufmann and Pierre-Maxence Vaysse and Adele Savage and Loes FS Kooreman and Natasja Janssen and Sonal Varma and Kevin Yi Mi Ren and Shaila Merchant and Cecil Jay Engel and Steven WM Olde Damink and Marjolein L Smidt and Sami Shousha and Hemali Chauhan and Evdoxia Karali and Emine Kazanc and George Poulogiannis and Gabor Fichtinger and Boglárka Tauber and Daniel R Leff and Steven D Pringle and John F Rudan and Ron MA Heeren and Tiffany Porta Siegel and Zoltán Takáts and Júlia Balog},
year = {2024},
date = {2024-01-01},
journal = {British Journal of Cancer},
pages = {1-11},
publisher = {Nature Publishing Group UK},
abstract = {Background
While REIMS technology has successfully been demonstrated for the histological identification of ex-vivo breast tumor tissues, questions regarding the robustness of the approach and the possibility of tumor molecular diagnostics still remain unanswered. In the current study, we set out to determine whether it is possible to acquire cross-comparable REIMS datasets at multiple sites for the identification of breast tumors and subtypes.
Methods
A consortium of four sites with three of them having access to fresh surgical tissue samples performed tissue analysis using identical REIMS setups and protocols. Overall, 21 breast cancer specimens containing pathology-validated tumor and adipose tissues were analyzed and results were compared using uni- and multivariate statistics on normal, WT and PIK3CA mutant ductal carcinomas.
Results
Statistical analysis of data from standards showed significant …},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Background
While REIMS technology has successfully been demonstrated for the histological identification of ex-vivo breast tumor tissues, questions regarding the robustness of the approach and the possibility of tumor molecular diagnostics still remain unanswered. In the current study, we set out to determine whether it is possible to acquire cross-comparable REIMS datasets at multiple sites for the identification of breast tumors and subtypes.
Methods
A consortium of four sites with three of them having access to fresh surgical tissue samples performed tissue analysis using identical REIMS setups and protocols. Overall, 21 breast cancer specimens containing pathology-validated tumor and adipose tissues were analyzed and results were compared using uni- and multivariate statistics on normal, WT and PIK3CA mutant ductal carcinomas.
Results
Statistical analysis of data from standards showed significant …
While REIMS technology has successfully been demonstrated for the histological identification of ex-vivo breast tumor tissues, questions regarding the robustness of the approach and the possibility of tumor molecular diagnostics still remain unanswered. In the current study, we set out to determine whether it is possible to acquire cross-comparable REIMS datasets at multiple sites for the identification of breast tumors and subtypes.
Methods
A consortium of four sites with three of them having access to fresh surgical tissue samples performed tissue analysis using identical REIMS setups and protocols. Overall, 21 breast cancer specimens containing pathology-validated tumor and adipose tissues were analyzed and results were compared using uni- and multivariate statistics on normal, WT and PIK3CA mutant ductal carcinomas.
Results
Statistical analysis of data from standards showed significant …
Hashtrudi-Zaad, Kian; Ungi, Tamas; Yeung, Chris; Baum, Zachary; Cernelev, Pavel-Dumitru; Hage, Anthony N; Schlenger, Christopher; Fichtinger, Gabor
Expert-guided optimization of ultrasound segmentation models for 3D spine imaging Journal Article
In: pp. 680-685, 2024.
@article{hashtrudi-zaad2024,
title = {Expert-guided optimization of ultrasound segmentation models for 3D spine imaging},
author = {Kian Hashtrudi-Zaad and Tamas Ungi and Chris Yeung and Zachary Baum and Pavel-Dumitru Cernelev and Anthony N Hage and Christopher Schlenger and Gabor Fichtinger},
year = {2024},
date = {2024-01-01},
pages = {680-685},
publisher = {IEEE},
abstract = {We explored ultrasound for imaging bones, specifically the spine, as a safer and more accessible alternative to conventional X-ray. We aimed to improve how well deep learning segmentation models filter bone signals from ultrasound frames with the goal of using these segmented images for reconstructing the 3-dimensional spine volume.Our dataset consisted of spatially tracked ultrasound scans from 25 patients. Image frames from these scans were also manually annotated to provide training data for image segmentation deep learning. To find the optimal automatic segmentation method, we assessed five different artificial neural network models and their variations by hyperparameter tuning. Our main contribution is a new approach for model selection, employing an Elo rating system to efficiently rank trained models based on their visual performance as assessed by clinical users. This method addresses the …},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
We explored ultrasound for imaging bones, specifically the spine, as a safer and more accessible alternative to conventional X-ray. We aimed to improve how well deep learning segmentation models filter bone signals from ultrasound frames with the goal of using these segmented images for reconstructing the 3-dimensional spine volume.Our dataset consisted of spatially tracked ultrasound scans from 25 patients. Image frames from these scans were also manually annotated to provide training data for image segmentation deep learning. To find the optimal automatic segmentation method, we assessed five different artificial neural network models and their variations by hyperparameter tuning. Our main contribution is a new approach for model selection, employing an Elo rating system to efficiently rank trained models based on their visual performance as assessed by clinical users. This method addresses the …
Yang, Jianming; Hisey, Rebecca; Bierbrier, Joshua; Law, Christine; Fichtinger, Gabor; Holden, Matthew
Frame Selection Methods to Streamline Surgical Video Annotation for Tool Detection Tasks Journal Article
In: pp. 892-898, 2024.
@article{yang2024,
title = {Frame Selection Methods to Streamline Surgical Video Annotation for Tool Detection Tasks},
author = {Jianming Yang and Rebecca Hisey and Joshua Bierbrier and Christine Law and Gabor Fichtinger and Matthew Holden},
year = {2024},
date = {2024-01-01},
pages = {892-898},
publisher = {IEEE},
abstract = {Given the growing volume of surgical data and the increasing demand for annotation, there is a pressing need to streamline the annotation process for surgical videos. Previously, annotation tools for object detection tasks have greatly evolved, reducing time expense and enhancing ease. There are also many initial frame selection approaches for Artificial Intelligence (AI) assisted annotation tasks to further reduce human effort. However, these methods have rarely been implemented and reported in the context of surgical datasets, especially in cataract surgery datasets. The identification of initial frames to annotate before the use of any tools or algorithms determines annotation efficiency. Therefore, in this paper, we chose to prioritize the development of a method for selecting initial frames to facilitate the subsequent automated annotation process. We propose a customized initial frames selection method based on …},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Given the growing volume of surgical data and the increasing demand for annotation, there is a pressing need to streamline the annotation process for surgical videos. Previously, annotation tools for object detection tasks have greatly evolved, reducing time expense and enhancing ease. There are also many initial frame selection approaches for Artificial Intelligence (AI) assisted annotation tasks to further reduce human effort. However, these methods have rarely been implemented and reported in the context of surgical datasets, especially in cataract surgery datasets. The identification of initial frames to annotate before the use of any tools or algorithms determines annotation efficiency. Therefore, in this paper, we chose to prioritize the development of a method for selecting initial frames to facilitate the subsequent automated annotation process. We propose a customized initial frames selection method based on …
Barr, Colton; Groves, Leah; Ungi, Tamas; Siemens, D Robert; Diao, Babacar; Kikinis, Ron; Mousavi, Parvin; Fichtinger, Gabor
Extracting 3D Prostate Geometry from 2D Optically-Tracked Transrectal Ultrasound Images Journal Article
In: pp. 32-37, 2024.
@article{barr2024,
title = {Extracting 3D Prostate Geometry from 2D Optically-Tracked Transrectal Ultrasound Images},
author = {Colton Barr and Leah Groves and Tamas Ungi and D Robert Siemens and Babacar Diao and Ron Kikinis and Parvin Mousavi and Gabor Fichtinger},
year = {2024},
date = {2024-01-01},
pages = {32-37},
publisher = {IEEE},
abstract = {The technical challenges of traditional transrectal ultrasound-guided prostate biopsy, combined with the limited availability of more advanced prostate imaging techniques, have exacerbated existing differences in prostate cancer outcomes between high-resource and low-resource healthcare settings. The objective of this paper is to improve the tools available to clinicians in low-resource settings by working towards an inexpensive ultrasound-guided prostate biopsy navigation system. The principal contributions detailed here are the design, implementation, and testing of a system capable of generating a 3D model of the prostate from spatially-tracked 2D ultrasound images. The system uses open-source software, low-cost materials, and deep learning to segment and localize cross-sections of the prostate in order to produce a patient-specific 3D prostate model. A user study was performed to evaluate the …},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The technical challenges of traditional transrectal ultrasound-guided prostate biopsy, combined with the limited availability of more advanced prostate imaging techniques, have exacerbated existing differences in prostate cancer outcomes between high-resource and low-resource healthcare settings. The objective of this paper is to improve the tools available to clinicians in low-resource settings by working towards an inexpensive ultrasound-guided prostate biopsy navigation system. The principal contributions detailed here are the design, implementation, and testing of a system capable of generating a 3D model of the prostate from spatially-tracked 2D ultrasound images. The system uses open-source software, low-cost materials, and deep learning to segment and localize cross-sections of the prostate in order to produce a patient-specific 3D prostate model. A user study was performed to evaluate the …
Bumm, Rudolf; Zaffino, Paolo; Lasso, Andras; Estépar, Raúl San José; Pieper, Steven; Wasserthal, Jakob; Spadea, Maria Francesca; Latshang, Tsogyal; Kawel-Boehm, Nadine; Wäckerlin, Adrian; Werner, Raphael; Hässig, Gabriela; Furrer, Markus; Kikinis, Ron
Artificial intelligence (AI)-assisted chest computer tomography (CT) insights: a study on intensive care unit (ICU) admittance trends in 78 coronavirus disease 2019 (COVID-19 … Journal Article
In: Journal of Thoracic Disease, vol. 16, no. 2, 2024.
@article{bumm2024,
title = {Artificial intelligence (AI)-assisted chest computer tomography (CT) insights: a study on intensive care unit (ICU) admittance trends in 78 coronavirus disease 2019 (COVID-19 …},
author = {Rudolf Bumm and Paolo Zaffino and Andras Lasso and Raúl San José Estépar and Steven Pieper and Jakob Wasserthal and Maria Francesca Spadea and Tsogyal Latshang and Nadine Kawel-Boehm and Adrian Wäckerlin and Raphael Werner and Gabriela Hässig and Markus Furrer and Ron Kikinis},
year = {2024},
date = {2024-01-01},
journal = {Journal of Thoracic Disease},
volume = {16},
number = {2},
publisher = {AME Publishing Company},
abstract = {Background: The global coronavirus disease 2019 (COVID-19) pandemic has posed substantial challenges for healthcare systems, notably the increased demand for chest computed tomography (CT) scans, which lack automated analysis. Our study addresses this by utilizing artificial intelligence-supported automated computer analysis to investigate lung involvement distribution and extent in COVID-19 patients. Additionally, we explore the association between lung involvement and intensive care unit (ICU) admission, while also comparing computer analysis performance with expert radiologists’ assessments.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Background: The global coronavirus disease 2019 (COVID-19) pandemic has posed substantial challenges for healthcare systems, notably the increased demand for chest computed tomography (CT) scans, which lack automated analysis. Our study addresses this by utilizing artificial intelligence-supported automated computer analysis to investigate lung involvement distribution and extent in COVID-19 patients. Additionally, we explore the association between lung involvement and intensive care unit (ICU) admission, while also comparing computer analysis performance with expert radiologists’ assessments.
Herz, Christian; Vergnet, Nicolas; Tian, Sijie; Aly, Abdullah H; Jolley, Matthew A; Tran, Nathanael; Arenas, Gabriel; Lasso, Andras; Schwartz, Nadav; O’Neill, Kathleen E; Yushkevich, Paul A; Pouch, Alison M
Open-source graphical user interface for the creation of synthetic skeletons for medical image analysis Journal Article
In: Journal of Medical Imaging, vol. 11, no. 3, pp. 036001-036001, 2024.
@article{herz2024,
title = {Open-source graphical user interface for the creation of synthetic skeletons for medical image analysis},
author = {Christian Herz and Nicolas Vergnet and Sijie Tian and Abdullah H Aly and Matthew A Jolley and Nathanael Tran and Gabriel Arenas and Andras Lasso and Nadav Schwartz and Kathleen E O’Neill and Paul A Yushkevich and Alison M Pouch},
year = {2024},
date = {2024-01-01},
journal = {Journal of Medical Imaging},
volume = {11},
number = {3},
pages = {036001-036001},
publisher = {Society of Photo-Optical Instrumentation Engineers},
abstract = {Purpose
Deformable medial modeling is an inverse skeletonization approach to representing anatomy in medical images, which can be used for statistical shape analysis and assessment of patient-specific anatomical features such as locally varying thickness. It involves deforming a pre-defined synthetic skeleton, or template, to anatomical structures of the same class. The lack of software for creating such skeletons has been a limitation to more widespread use of deformable medial modeling. Therefore, the objective of this work is to present an open-source user interface (UI) for the creation of synthetic skeletons for a range of medial modeling applications in medical imaging.
Approach
A UI for interactive design of synthetic skeletons was implemented in 3D Slicer, an open-source medical image analysis application. The steps in synthetic skeleton design include importation and skeletonization of a 3D …},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Purpose
Deformable medial modeling is an inverse skeletonization approach to representing anatomy in medical images, which can be used for statistical shape analysis and assessment of patient-specific anatomical features such as locally varying thickness. It involves deforming a pre-defined synthetic skeleton, or template, to anatomical structures of the same class. The lack of software for creating such skeletons has been a limitation to more widespread use of deformable medial modeling. Therefore, the objective of this work is to present an open-source user interface (UI) for the creation of synthetic skeletons for a range of medial modeling applications in medical imaging.
Approach
A UI for interactive design of synthetic skeletons was implemented in 3D Slicer, an open-source medical image analysis application. The steps in synthetic skeleton design include importation and skeletonization of a 3D …
Deformable medial modeling is an inverse skeletonization approach to representing anatomy in medical images, which can be used for statistical shape analysis and assessment of patient-specific anatomical features such as locally varying thickness. It involves deforming a pre-defined synthetic skeleton, or template, to anatomical structures of the same class. The lack of software for creating such skeletons has been a limitation to more widespread use of deformable medial modeling. Therefore, the objective of this work is to present an open-source user interface (UI) for the creation of synthetic skeletons for a range of medial modeling applications in medical imaging.
Approach
A UI for interactive design of synthetic skeletons was implemented in 3D Slicer, an open-source medical image analysis application. The steps in synthetic skeleton design include importation and skeletonization of a 3D …
Kaufmann, Martin; Jamzad, Amoon; Ungi, Tamas; Rodgers, Jessica; Koster, Teaghan; Chris, Yeung; Janssen, Natasja; McMullen, Julie; Solberg, Kathryn; Cheesman, Joanna; Ren, Kevin Ti Mi; Varma, Sonal; Merchant, Shaila; Engel, Cecil Jay; Walker, G Ross; Gallo, Andrea; Jabs, Doris; Mousavi, Parvin; Fichtinger, Gabor; Rudan, John
Three-dimensional navigated mass spectrometry for intraoperative margin assessment during breast cancer surgery Journal Article
In: vol. 31, iss. 1, pp. S10-S10, 2024.
@article{fichtinger2024i,
title = {Three-dimensional navigated mass spectrometry for intraoperative margin assessment during breast cancer surgery},
author = {Martin Kaufmann and Amoon Jamzad and Tamas Ungi and Jessica Rodgers and Teaghan Koster and Yeung Chris and Natasja Janssen and Julie McMullen and Kathryn Solberg and Joanna Cheesman and Kevin Ti Mi Ren and Sonal Varma and Shaila Merchant and Cecil Jay Engel and G Ross Walker and Andrea Gallo and Doris Jabs and Parvin Mousavi and Gabor Fichtinger and John Rudan},
url = {https://scholar.google.com/scholar?cluster=16985799098796735653&hl=en&oi=scholarr},
year = {2024},
date = {2024-01-01},
volume = {31},
issue = {1},
pages = {S10-S10},
publisher = {SPRINGER},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Hintz, Lucas; Nanziri, Sarah C; Dance, Sarah; Jawed, Kochai; Oetgen, Matthew; Ungi, Tamas; Fichtinger, Gabor; Schlenger, Christopher; Cleary, Kevin
3D volume reconstruction for pediatric scoliosis evaluation using motion-tracked ultrasound Journal Article
In: vol. 12928, pp. 223-227, 2024.
@article{fichtinger2024g,
title = {3D volume reconstruction for pediatric scoliosis evaluation using motion-tracked ultrasound},
author = {Lucas Hintz and Sarah C Nanziri and Sarah Dance and Kochai Jawed and Matthew Oetgen and Tamas Ungi and Gabor Fichtinger and Christopher Schlenger and Kevin Cleary},
url = {https://www.spiedigitallibrary.org/conference-proceedings-of-spie/12928/1292811/3D-volume-reconstruction-for-pediatric-scoliosis-evaluation-using-motion-tracked/10.1117/12.3008629.short},
year = {2024},
date = {2024-01-01},
volume = {12928},
pages = {223-227},
publisher = {SPIE},
abstract = {We have evaluated AI-segmented 3D spine ultrasound for scoliosis measurement in a feasibility study of pediatric patients enrolled over two months in the orthopedic clinic at Children’s National Hospital. Patients who presented to clinic for scoliosis evaluation were invited to participate and their spines were scanned using the method. Our system consists of three Optitrack cameras which track a Clarius wireless ultrasound probe and infrared marked waistbelt. Proprietary SpineUs software uses neural networks to build a volumetric reproduction of the spine in real-time using a laptop computer. We can approximate the maximal lateral curvature using the transverse process angle of the virtual reconstruction; these angles were compared to those from the radiographic exams for each patient from the same visit. Scans and radiographs from five patients were examined and demonstrate a linear correlation between …},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
We have evaluated AI-segmented 3D spine ultrasound for scoliosis measurement in a feasibility study of pediatric patients enrolled over two months in the orthopedic clinic at Children’s National Hospital. Patients who presented to clinic for scoliosis evaluation were invited to participate and their spines were scanned using the method. Our system consists of three Optitrack cameras which track a Clarius wireless ultrasound probe and infrared marked waistbelt. Proprietary SpineUs software uses neural networks to build a volumetric reproduction of the spine in real-time using a laptop computer. We can approximate the maximal lateral curvature using the transverse process angle of the virtual reconstruction; these angles were compared to those from the radiographic exams for each patient from the same visit. Scans and radiographs from five patients were examined and demonstrate a linear correlation between …
d'Albenzio, Gabriella; Hisey, Rebecca; Srikanthan, Dilakshan; Ungi, Tamas; Lasso, Andras; Aghayan, Davit; Fichtinger, Gabor; Palomar, Rafael
Using NURBS for virtual resections in liver surgery planning: a comparative usability study Journal Article
In: vol. 12927, pp. 235-241, 2024.
@article{fichtinger2024f,
title = {Using NURBS for virtual resections in liver surgery planning: a comparative usability study},
author = {Gabriella d'Albenzio and Rebecca Hisey and Dilakshan Srikanthan and Tamas Ungi and Andras Lasso and Davit Aghayan and Gabor Fichtinger and Rafael Palomar},
url = {https://www.spiedigitallibrary.org/conference-proceedings-of-spie/12927/129270Z/Using-NURBS-for-virtual-resections-in-liver-surgery-planning/10.1117/12.3006486.short},
year = {2024},
date = {2024-01-01},
volume = {12927},
pages = {235-241},
publisher = {SPIE},
abstract = {PURPOSE
Accurate preoperative planning is crucial for liver resection surgery due to the complex anatomical structures and variations among patients. The need of virtual resections utilizing deformable surfaces presents a promising approach for effective liver surgery planning. However, the range of available surface definitions poses the question of which definition is most appropriate.
METHODS
The study compares the use of NURBS and B´ezier surfaces for the definition of virtual resections through a usability study, where 25 participants (19 biomedical researchers and 6 liver surgeons) completed tasks using varying numbers of control points driving surface deformations and different surface types. Specifically, participants aim to perform virtual liver resections using 16 and 9 control points for NURBS and B´ezier surfaces. The goal is to assess whether they can attain an optimal resection plan, effectively …},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
PURPOSE
Accurate preoperative planning is crucial for liver resection surgery due to the complex anatomical structures and variations among patients. The need of virtual resections utilizing deformable surfaces presents a promising approach for effective liver surgery planning. However, the range of available surface definitions poses the question of which definition is most appropriate.
METHODS
The study compares the use of NURBS and B´ezier surfaces for the definition of virtual resections through a usability study, where 25 participants (19 biomedical researchers and 6 liver surgeons) completed tasks using varying numbers of control points driving surface deformations and different surface types. Specifically, participants aim to perform virtual liver resections using 16 and 9 control points for NURBS and B´ezier surfaces. The goal is to assess whether they can attain an optimal resection plan, effectively …
Accurate preoperative planning is crucial for liver resection surgery due to the complex anatomical structures and variations among patients. The need of virtual resections utilizing deformable surfaces presents a promising approach for effective liver surgery planning. However, the range of available surface definitions poses the question of which definition is most appropriate.
METHODS
The study compares the use of NURBS and B´ezier surfaces for the definition of virtual resections through a usability study, where 25 participants (19 biomedical researchers and 6 liver surgeons) completed tasks using varying numbers of control points driving surface deformations and different surface types. Specifically, participants aim to perform virtual liver resections using 16 and 9 control points for NURBS and B´ezier surfaces. The goal is to assess whether they can attain an optimal resection plan, effectively …
Connolly, Laura; Fooladgar, Fahimeh; Jamzad, Amoon; Kaufmann, Martin; Syeda, Ayesha; Ren, Kevin; Abolmaesumi, Purang; Rudan, John F; McKay, Doug; Fichtinger, Gabor; Mousavi, Parvin
ImSpect: Image-driven self-supervised learning for surgical margin evaluation with mass spectrometry Journal Article
In: International Journal of Computer Assisted Radiology and Surgery, pp. 1-8, 2024.
@article{fichtinger2024e,
title = {ImSpect: Image-driven self-supervised learning for surgical margin evaluation with mass spectrometry},
author = {Laura Connolly and Fahimeh Fooladgar and Amoon Jamzad and Martin Kaufmann and Ayesha Syeda and Kevin Ren and Purang Abolmaesumi and John F Rudan and Doug McKay and Gabor Fichtinger and Parvin Mousavi},
url = {https://link.springer.com/article/10.1007/s11548-024-03106-1},
year = {2024},
date = {2024-01-01},
journal = {International Journal of Computer Assisted Radiology and Surgery},
pages = {1-8},
publisher = {Springer International Publishing},
abstract = {Purpose
Real-time assessment of surgical margins is critical for favorable outcomes in cancer patients. The iKnife is a mass spectrometry device that has demonstrated potential for margin detection in cancer surgery. Previous studies have shown that using deep learning on iKnife data can facilitate real-time tissue characterization. However, none of the existing literature on the iKnife facilitate the use of publicly available, state-of-the-art pretrained networks or datasets that have been used in computer vision and other domains.
Methods
In a new framework we call ImSpect, we convert 1D iKnife data, captured during basal cell carcinoma (BCC) surgery, into 2D images in order to capitalize on state-of-the-art image classification networks. We also use self-supervision to leverage large amounts of unlabeled, intraoperative data to accommodate the data requirements of these networks.
Results
Through extensive ablation …},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Purpose
Real-time assessment of surgical margins is critical for favorable outcomes in cancer patients. The iKnife is a mass spectrometry device that has demonstrated potential for margin detection in cancer surgery. Previous studies have shown that using deep learning on iKnife data can facilitate real-time tissue characterization. However, none of the existing literature on the iKnife facilitate the use of publicly available, state-of-the-art pretrained networks or datasets that have been used in computer vision and other domains.
Methods
In a new framework we call ImSpect, we convert 1D iKnife data, captured during basal cell carcinoma (BCC) surgery, into 2D images in order to capitalize on state-of-the-art image classification networks. We also use self-supervision to leverage large amounts of unlabeled, intraoperative data to accommodate the data requirements of these networks.
Results
Through extensive ablation …
Real-time assessment of surgical margins is critical for favorable outcomes in cancer patients. The iKnife is a mass spectrometry device that has demonstrated potential for margin detection in cancer surgery. Previous studies have shown that using deep learning on iKnife data can facilitate real-time tissue characterization. However, none of the existing literature on the iKnife facilitate the use of publicly available, state-of-the-art pretrained networks or datasets that have been used in computer vision and other domains.
Methods
In a new framework we call ImSpect, we convert 1D iKnife data, captured during basal cell carcinoma (BCC) surgery, into 2D images in order to capitalize on state-of-the-art image classification networks. We also use self-supervision to leverage large amounts of unlabeled, intraoperative data to accommodate the data requirements of these networks.
Results
Through extensive ablation …
Yeung, Chris; Ungi, Tamas; Hu, Zoe; Jamzad, Amoon; Kaufmann, Martin; Walker, Ross; Merchant, Shaila; Engel, Cecil Jay; Jabs, Doris; Rudan, John; Mousavi, Parvin; Fichtinger, Gabor
From quantitative metrics to clinical success: assessing the utility of deep learning for tumor segmentation in breast surgery Journal Article
In: International Journal of Computer Assisted Radiology and Surgery, pp. 1-9, 2024.
@article{yeung2024,
title = {From quantitative metrics to clinical success: assessing the utility of deep learning for tumor segmentation in breast surgery},
author = {Chris Yeung and Tamas Ungi and Zoe Hu and Amoon Jamzad and Martin Kaufmann and Ross Walker and Shaila Merchant and Cecil Jay Engel and Doris Jabs and John Rudan and Parvin Mousavi and Gabor Fichtinger},
url = {https://link.springer.com/article/10.1007/s11548-024-03133-y},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {International Journal of Computer Assisted Radiology and Surgery},
pages = {1-9},
publisher = {Springer International Publishing},
abstract = {Purpose
Preventing positive margins is essential for ensuring favorable patient outcomes following breast-conserving surgery (BCS). Deep learning has the potential to enable this by automatically contouring the tumor and guiding resection in real time. However, evaluation of such models with respect to pathology outcomes is necessary for their successful translation into clinical practice.
Methods
Sixteen deep learning models based on established architectures in the literature are trained on 7318 ultrasound images from 33 patients. Models are ranked by an expert based on their contours generated from images in our test set. Generated contours from each model are also analyzed using recorded cautery trajectories of five navigated BCS cases to predict margin status. Predicted margins are compared with pathology reports.
Results
The best-performing model using both quantitative evaluation and our visual …},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Purpose
Preventing positive margins is essential for ensuring favorable patient outcomes following breast-conserving surgery (BCS). Deep learning has the potential to enable this by automatically contouring the tumor and guiding resection in real time. However, evaluation of such models with respect to pathology outcomes is necessary for their successful translation into clinical practice.
Methods
Sixteen deep learning models based on established architectures in the literature are trained on 7318 ultrasound images from 33 patients. Models are ranked by an expert based on their contours generated from images in our test set. Generated contours from each model are also analyzed using recorded cautery trajectories of five navigated BCS cases to predict margin status. Predicted margins are compared with pathology reports.
Results
The best-performing model using both quantitative evaluation and our visual …
Preventing positive margins is essential for ensuring favorable patient outcomes following breast-conserving surgery (BCS). Deep learning has the potential to enable this by automatically contouring the tumor and guiding resection in real time. However, evaluation of such models with respect to pathology outcomes is necessary for their successful translation into clinical practice.
Methods
Sixteen deep learning models based on established architectures in the literature are trained on 7318 ultrasound images from 33 patients. Models are ranked by an expert based on their contours generated from images in our test set. Generated contours from each model are also analyzed using recorded cautery trajectories of five navigated BCS cases to predict margin status. Predicted margins are compared with pathology reports.
Results
The best-performing model using both quantitative evaluation and our visual …
Kaufmann, Martin; Jamzad, Amoon; Ungi, Tamas; Rodgers, Jessica R; Koster, Teaghan; Yeung, Chris; Ehrlich, Josh; Santilli, Alice; Asselin, Mark; Janssen, Natasja; McMullen, Julie; Solberg, Kathryn; Cheesman, Joanna; Carlo, Alessia Di; Ren, Kevin Yi Mi; Varma, Sonal; Merchant, Shaila; Engel, Cecil Jay; Walker, G Ross; Gallo, Andrea; Jabs, Doris; Mousavi, Parvin; Fichtinger, Gabor; Rudan, John F
Abstract PO2-23-07: Three-dimensional navigated mass spectrometry for intraoperative margin assessment during breast cancer surgery Journal Article
In: Cancer Research, vol. 84, iss. 9_Supplement, pp. PO2-23-07-PO2-23-07, 2024.
@article{fichtinger2024c,
title = {Abstract PO2-23-07: Three-dimensional navigated mass spectrometry for intraoperative margin assessment during breast cancer surgery},
author = {Martin Kaufmann and Amoon Jamzad and Tamas Ungi and Jessica R Rodgers and Teaghan Koster and Chris Yeung and Josh Ehrlich and Alice Santilli and Mark Asselin and Natasja Janssen and Julie McMullen and Kathryn Solberg and Joanna Cheesman and Alessia Di Carlo and Kevin Yi Mi Ren and Sonal Varma and Shaila Merchant and Cecil Jay Engel and G Ross Walker and Andrea Gallo and Doris Jabs and Parvin Mousavi and Gabor Fichtinger and John F Rudan},
url = {https://aacrjournals.org/cancerres/article/84/9_Supplement/PO2-23-07/743683},
year = {2024},
date = {2024-01-01},
journal = {Cancer Research},
volume = {84},
issue = {9_Supplement},
pages = {PO2-23-07-PO2-23-07},
publisher = {The American Association for Cancer Research},
abstract = {Positive resection margins occur in approximately 25% of breast cancer (BCa) surgeries, requiring re-operation. Margin status is not routinely available during surgery; thus, technologies that identify residual cancer on the specimen or cavity are needed to provide intraoperative decision support that may reduce positive margin rates. Rapid evaporative ionization mass spectrometry (REIMS) is an emerging technique that chemically profiles the plume generated by tissue cauterization to classify the ablated tissue as either cancerous or non-cancerous, on the basis of detected lipid species. Although REIMS can distinguish cancer and non-cancerous breast tissue by the signals generated, it does not indicate the location of the classified tissue in real-time. Our objective was to combine REIMS with spatio-temporal navigation (navigated REIMS), and to compare performance of navigated REIMS with conventional …},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Positive resection margins occur in approximately 25% of breast cancer (BCa) surgeries, requiring re-operation. Margin status is not routinely available during surgery; thus, technologies that identify residual cancer on the specimen or cavity are needed to provide intraoperative decision support that may reduce positive margin rates. Rapid evaporative ionization mass spectrometry (REIMS) is an emerging technique that chemically profiles the plume generated by tissue cauterization to classify the ablated tissue as either cancerous or non-cancerous, on the basis of detected lipid species. Although REIMS can distinguish cancer and non-cancerous breast tissue by the signals generated, it does not indicate the location of the classified tissue in real-time. Our objective was to combine REIMS with spatio-temporal navigation (navigated REIMS), and to compare performance of navigated REIMS with conventional …