Orosz, Gábor; Szabó, Róbert Zsolt; Ungi, Tamás; Barr, Colton; Yeung, Chris; Fichtinger, Gábor; Gál, János; Haidegger, Tamás
Lung Ultrasound Imaging and Image Processing with Artificial Intelligence Methods for Bedside Diagnostic Examinations Journal Article
In: Acta Polytechnica Hungarica, vol. 20, iss. 8, 2023.
@article{fichtinger2023d,
title = {Lung Ultrasound Imaging and Image Processing with Artificial Intelligence Methods for Bedside Diagnostic Examinations},
author = {Gábor Orosz and Róbert Zsolt Szabó and Tamás Ungi and Colton Barr and Chris Yeung and Gábor Fichtinger and János Gál and Tamás Haidegger},
url = {https://acta.uni-obuda.hu/Orosz_Szabo_Ungi_Barr_Yeung_Fichtinger_Gal_Haidegger_137.pdf},
year = {2023},
date = {2023-01-01},
journal = {Acta Polytechnica Hungarica},
volume = {20},
issue = {8},
abstract = {Artificial Intelligence-assisted radiology has shown to offer significant benefits in clinical care. Physicians often face challenges in identifying the underlying causes of acute respiratory failure. One method employed by experts is the utilization of bedside lung ultrasound, although it has a significant learning curve. In our study, we explore the potential of a Machine Learning-based automated decision-support system to assist inexperienced practitioners in interpreting lung ultrasound scans. This system incorporates medical ultrasound, advanced data processing techniques, and a neural network implementation to achieve its objective. The article provides a comprehensive overview of the steps involved in data preparation and the implementation of the neural network. The accuracy and error rate of the most effective model are presented, accompanied by illustrative examples of their predictions. Furthermore, the paper concludes with an evaluation of the results, identification of limitations, and recommendations for future enhancements.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Artificial Intelligence-assisted radiology has shown to offer significant benefits in clinical care. Physicians often face challenges in identifying the underlying causes of acute respiratory failure. One method employed by experts is the utilization of bedside lung ultrasound, although it has a significant learning curve. In our study, we explore the potential of a Machine Learning-based automated decision-support system to assist inexperienced practitioners in interpreting lung ultrasound scans. This system incorporates medical ultrasound, advanced data processing techniques, and a neural network implementation to achieve its objective. The article provides a comprehensive overview of the steps involved in data preparation and the implementation of the neural network. The accuracy and error rate of the most effective model are presented, accompanied by illustrative examples of their predictions. Furthermore, the paper concludes with an evaluation of the results, identification of limitations, and recommendations for future enhancements.
Nam, Hannah H; Flynn, Maura; Lasso, Andras; Herz, Christian; Sabin, Patricia; Wang, Yan; Cianciulli, Alana; Vigil, Chad; Huang, Jing; Vicory, Jared; Paniagua, Beatriz; Allemang, David; Goldberg, David J; Nuri, Mohammed; Cohen, Meryl S; Fichtinger, Gabor; Jolley, Matthew A
Modeling of the tricuspid valve and right ventricle in hypoplastic left heart syndrome with a Fontan circulation Journal Article
In: Circulation: Cardiovascular Imaging, vol. 16, iss. 3, pp. e014671, 2023.
@article{fichtinger2023c,
title = {Modeling of the tricuspid valve and right ventricle in hypoplastic left heart syndrome with a Fontan circulation},
author = {Hannah H Nam and Maura Flynn and Andras Lasso and Christian Herz and Patricia Sabin and Yan Wang and Alana Cianciulli and Chad Vigil and Jing Huang and Jared Vicory and Beatriz Paniagua and David Allemang and David J Goldberg and Mohammed Nuri and Meryl S Cohen and Gabor Fichtinger and Matthew A Jolley},
url = {https://www.ahajournals.org/doi/abs/10.1161/CIRCIMAGING.122.014671},
year = {2023},
date = {2023-01-01},
journal = {Circulation: Cardiovascular Imaging},
volume = {16},
issue = {3},
pages = {e014671},
publisher = {Lippincott Williams & Wilkins},
abstract = {Background
In hypoplastic left heart syndrome, tricuspid regurgitation (TR) is associated with circulatory failure and death. We hypothesized that the tricuspid valve (TV) structure of patients with hypoplastic left heart syndrome with a Fontan circulation and moderate or greater TR differs from those with mild or less TR, and that right ventricle volume is associated with TV structure and dysfunction.
Methods
TV of 100 patients with hypoplastic left heart syndrome and a Fontan circulation were modeled using transthoracic 3-dimensional echocardiograms and custom software in SlicerHeart. Associations of TV structure to TR grade and right ventricle function and volume were investigated. Shape parameterization and analysis was used to calculate the mean shape of the TV leaflets, their principal modes of variation, and to characterize associations of TV leaflet shape to TR.
Results
In univariate modeling, patients with …},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Background
In hypoplastic left heart syndrome, tricuspid regurgitation (TR) is associated with circulatory failure and death. We hypothesized that the tricuspid valve (TV) structure of patients with hypoplastic left heart syndrome with a Fontan circulation and moderate or greater TR differs from those with mild or less TR, and that right ventricle volume is associated with TV structure and dysfunction.
Methods
TV of 100 patients with hypoplastic left heart syndrome and a Fontan circulation were modeled using transthoracic 3-dimensional echocardiograms and custom software in SlicerHeart. Associations of TV structure to TR grade and right ventricle function and volume were investigated. Shape parameterization and analysis was used to calculate the mean shape of the TV leaflets, their principal modes of variation, and to characterize associations of TV leaflet shape to TR.
Results
In univariate modeling, patients with …
In hypoplastic left heart syndrome, tricuspid regurgitation (TR) is associated with circulatory failure and death. We hypothesized that the tricuspid valve (TV) structure of patients with hypoplastic left heart syndrome with a Fontan circulation and moderate or greater TR differs from those with mild or less TR, and that right ventricle volume is associated with TV structure and dysfunction.
Methods
TV of 100 patients with hypoplastic left heart syndrome and a Fontan circulation were modeled using transthoracic 3-dimensional echocardiograms and custom software in SlicerHeart. Associations of TV structure to TR grade and right ventricle function and volume were investigated. Shape parameterization and analysis was used to calculate the mean shape of the TV leaflets, their principal modes of variation, and to characterize associations of TV leaflet shape to TR.
Results
In univariate modeling, patients with …
Kitner, Nicole; Rodgers, Jessica R; Ungi, Tamas; Korzeniowski, Martin; Olding, Timothy; Mousavi, Parvin; Fichtinger, Gabor
Multi-catheter modelling in reconstructed 3D transrectal ultrasound images from prostate brachytherapy Journal Article
In: vol. 12466, pp. 126-135, 2023.
@article{fichtinger2023b,
title = {Multi-catheter modelling in reconstructed 3D transrectal ultrasound images from prostate brachytherapy},
author = {Nicole Kitner and Jessica R Rodgers and Tamas Ungi and Martin Korzeniowski and Timothy Olding and Parvin Mousavi and Gabor Fichtinger},
url = {https://www.spiedigitallibrary.org/conference-proceedings-of-spie/12466/124660I/Multi-catheter-modelling-in-reconstructed-3D-transrectal-ultrasound-images-from/10.1117/12.2654019.short},
year = {2023},
date = {2023-01-01},
volume = {12466},
pages = {126-135},
publisher = {SPIE},
abstract = {High-dose-rate brachytherapy is an accepted standard-of-care treatment for prostate cancer. In this procedure, catheters are inserted using three-dimensional (3D) transrectal ultrasound image-guidance. Their positions are manually segmented for treatment planning and delivery. The transverse ultrasound sweep, which is subject to tip and depth error for catheter localization, is a commonly used ultrasound imaging option available for image acquisition. We propose a two-step pipeline that uses a deep-learning network and curve fitting to automatically localize and model catheters in transversely reconstructed 3D ultrasound images. In the first step, a 3D U-Net was trained to automatically segment all catheters in a 3D ultrasound image. Following this step, curve fitting was implemented to detect the shapes of individual catheters using polynomial fitting. Of the 343 catheters (from 20 patients) in the testing data, the …},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
High-dose-rate brachytherapy is an accepted standard-of-care treatment for prostate cancer. In this procedure, catheters are inserted using three-dimensional (3D) transrectal ultrasound image-guidance. Their positions are manually segmented for treatment planning and delivery. The transverse ultrasound sweep, which is subject to tip and depth error for catheter localization, is a commonly used ultrasound imaging option available for image acquisition. We propose a two-step pipeline that uses a deep-learning network and curve fitting to automatically localize and model catheters in transversely reconstructed 3D ultrasound images. In the first step, a 3D U-Net was trained to automatically segment all catheters in a 3D ultrasound image. Following this step, curve fitting was implemented to detect the shapes of individual catheters using polynomial fitting. Of the 343 catheters (from 20 patients) in the testing data, the …
Kaufmann, Martin; Iaboni, Natasha; Jamzad, Amoon; Hurlbut, David; Ren, Kevin Yi Mi; Rudan, John F; Mousavi, Parvin; Fichtinger, Gabor; Varma, Sonal; Caycedo-Marulanda, Antonio; Nicol, Christopher JB
In: Metabolites, vol. 13, iss. 4, pp. 508, 2023.
@article{fichtinger2023,
title = {Metabolically active zones involving fatty acid elongation delineated by DESI-MSI correlate with pathological and prognostic features of colorectal cancer},
author = {Martin Kaufmann and Natasha Iaboni and Amoon Jamzad and David Hurlbut and Kevin Yi Mi Ren and John F Rudan and Parvin Mousavi and Gabor Fichtinger and Sonal Varma and Antonio Caycedo-Marulanda and Christopher JB Nicol},
url = {https://www.mdpi.com/2218-1989/13/4/508},
year = {2023},
date = {2023-01-01},
journal = {Metabolites},
volume = {13},
issue = {4},
pages = {508},
publisher = {MDPI},
abstract = {Colorectal cancer (CRC) is the second leading cause of cancer deaths. Despite recent advances, five-year survival rates remain largely unchanged. Desorption electrospray ionization mass spectrometry imaging (DESI) is an emerging nondestructive metabolomics-based method that retains the spatial orientation of small-molecule profiles on tissue sections, which may be validated by ‘gold standard’ histopathology. In this study, CRC samples were analyzed by DESI from 10 patients undergoing surgery at Kingston Health Sciences Center. The spatial correlation of the mass spectral profiles was compared with histopathological annotations and prognostic biomarkers. Fresh frozen sections of representative colorectal cross sections and simulated endoscopic biopsy samples containing tumour and non-neoplastic mucosa for each patient were generated and analyzed by DESI in a blinded fashion. Sections were then hematoxylin and eosin (H and E) stained, annotated by two independent pathologists, and analyzed. Using PCA/LDA-based models, DESI profiles of the cross sections and biopsies achieved 97% and 75% accuracies in identifying the presence of adenocarcinoma, using leave-one-patient-out cross validation. Among the m/z ratios exhibiting the greatest differential abundance in adenocarcinoma were a series of eight long-chain or very-long-chain fatty acids, consistent with molecular and targeted metabolomics indicators of de novo lipogenesis in CRC tissue. Sample stratification based on the presence of lympovascular invasion (LVI), a poor CRC prognostic indicator, revealed the abundance of oxidized phospholipids, suggestive …},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Colorectal cancer (CRC) is the second leading cause of cancer deaths. Despite recent advances, five-year survival rates remain largely unchanged. Desorption electrospray ionization mass spectrometry imaging (DESI) is an emerging nondestructive metabolomics-based method that retains the spatial orientation of small-molecule profiles on tissue sections, which may be validated by ‘gold standard’ histopathology. In this study, CRC samples were analyzed by DESI from 10 patients undergoing surgery at Kingston Health Sciences Center. The spatial correlation of the mass spectral profiles was compared with histopathological annotations and prognostic biomarkers. Fresh frozen sections of representative colorectal cross sections and simulated endoscopic biopsy samples containing tumour and non-neoplastic mucosa for each patient were generated and analyzed by DESI in a blinded fashion. Sections were then hematoxylin and eosin (H and E) stained, annotated by two independent pathologists, and analyzed. Using PCA/LDA-based models, DESI profiles of the cross sections and biopsies achieved 97% and 75% accuracies in identifying the presence of adenocarcinoma, using leave-one-patient-out cross validation. Among the m/z ratios exhibiting the greatest differential abundance in adenocarcinoma were a series of eight long-chain or very-long-chain fatty acids, consistent with molecular and targeted metabolomics indicators of de novo lipogenesis in CRC tissue. Sample stratification based on the presence of lympovascular invasion (LVI), a poor CRC prognostic indicator, revealed the abundance of oxidized phospholipids, suggestive …
Fooladgar, Fahimeh; Jamzad, Amoon; Connolly, Laura; Santilli, Alice; Kaufmann, Martin; Ren, Kevin; Abolmaesumi, Purang; Rudan, John; McKay, Doug; Fichtinger, Gabor; Mousavi, Parvin
Uncertainty estimation for margin detection in cancer surgery using mass spectrometry Journal Article
In: International Journal of Computer Assisted Radiology and Surgery, 2022.
@article{Fooladgar2022,
title = {Uncertainty estimation for margin detection in cancer surgery using mass spectrometry},
author = {Fahimeh Fooladgar and Amoon Jamzad and Laura Connolly and Alice Santilli and Martin Kaufmann and Kevin Ren and Purang Abolmaesumi and John Rudan and Doug McKay and Gabor Fichtinger and Parvin Mousavi},
doi = {https://doi.org/10.1007/s11548-022-02764-3},
year = {2022},
date = {2022-09-01},
journal = {International Journal of Computer Assisted Radiology and Surgery},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Kitner, Nicole; Rodgers, Jessica R.; Ungi, Tamas; Olding, Timothy; Joshi, C. P.; Mousavi, Parvin; Fichtinger, Gabor; Korzeniowski, Martin
Automated catheter localization in ultrasound images from High-dose-rate prostate brachytherapy using deep learning and feature extraction Conference
Canadian Association for Radiation Oncologists (CARO) Annual Scientific Meeting, Canadian Association for Radiation Oncologists, 2022.
@conference{Kitner2022ab,
title = {Automated catheter localization in ultrasound images from High-dose-rate prostate brachytherapy using deep learning and feature extraction},
author = {Nicole Kitner and Jessica R. Rodgers and Tamas Ungi and Timothy Olding and C. P. Joshi and Parvin Mousavi and Gabor Fichtinger and Martin Korzeniowski},
year = {2022},
date = {2022-09-01},
urldate = {2022-09-01},
booktitle = {Canadian Association for Radiation Oncologists (CARO) Annual Scientific Meeting},
publisher = {Canadian Association for Radiation Oncologists},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Connolly, Laura; Degeut, Anton; Leonard, Simon; Tokuda, Junichi; Ungi, Tamas; Krieger, Axel; Kazanzides, Peter; Mousavi, Parvin; Fichtinger, Gabor; Taylor, Russell H.
Bridging 3D Slicer and ROS2 for Image-Guided Robotic Interventions Journal Article
In: Sensors, vol. 22, 2022.
@article{Connolly2022c,
title = {Bridging 3D Slicer and ROS2 for Image-Guided Robotic Interventions},
author = {Laura Connolly and Anton Degeut and Simon Leonard and Junichi Tokuda and Tamas Ungi and Axel Krieger and Peter Kazanzides and Parvin Mousavi and Gabor Fichtinger and Russell H. Taylor},
doi = {https://doi.org/10.3390/s22145336},
year = {2022},
date = {2022-07-01},
journal = {Sensors},
volume = {22},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Kitner, Nicole; Rodgers, Jessica R.; Ungi, Tamas; Korzeniowski, Martin; Olding, Timothy; Joshi, C. P.; Mousavi, Parvin; Fichtinger, Gabor
Automated Automatic catheter modelling in 3D transrectal ultrasound images from high-dose-rate prostate brachytherapy using a deep learning and feature extraction pipeline Conference
Canadian Organization of Medical Physicists (COMP) Annual Scientific Meeting, Canadian Organization of Medical Physicists, 2022.
@conference{Kitner2022a,
title = {Automated Automatic catheter modelling in 3D transrectal ultrasound images from high-dose-rate prostate brachytherapy using a deep learning and feature extraction pipeline},
author = {Nicole Kitner and Jessica R. Rodgers and Tamas Ungi and Martin Korzeniowski and Timothy Olding and C. P. Joshi and Parvin Mousavi and Gabor Fichtinger},
year = {2022},
date = {2022-06-01},
urldate = {2022-06-01},
booktitle = {Canadian Organization of Medical Physicists (COMP) Annual Scientific Meeting},
publisher = {Canadian Organization of Medical Physicists},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Hu, Zoe; Fauerbach, Paola V. Nasute; Yeung, Chris; Ungi, Tamas; Rudan, John; Engel, C. Jay; Mousavi, Parvin; Fichtinger, Gabor; Jabs, Doris
Real-time automatic tumor segmentation for ultrasound-guided breast-conserving surgery navigation Journal Article
In: International Journal of Computer Assisted Radiology and Surgery, vol. 17, no. 9, pp. 1663–1672, 2022.
@article{Hu2022,
title = {Real-time automatic tumor segmentation for ultrasound-guided breast-conserving surgery navigation},
author = {Zoe Hu and Paola V. Nasute Fauerbach and Chris Yeung and Tamas Ungi and John Rudan and C. Jay Engel and Parvin Mousavi and Gabor Fichtinger and Doris Jabs},
doi = {10.1007/s11548-022-02658-4},
year = {2022},
date = {2022-05-01},
urldate = {2022-05-01},
journal = {International Journal of Computer Assisted Radiology and Surgery},
volume = {17},
number = {9},
pages = {1663–1672},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Besio, Walt; Brankov, Jovan; Cerutti, Sergio; Cinar, Ali; Chen, Yu; Cobelli, Claudio; Coleman, Todd P; Davalos, Rafael; Ding, Lei; Farina, Dario; Fichtinger, Gabor; Gee, James; Guan, Cuntai; Harada, Kanado; Ilev, Ilko K; Food, US; Irazoqui, Pedro P; Ji, Jim; Kamper, Derek; Kiani, Mehdi; Li, Wen; Li, Xin; Liang, Jie; Linguraru, Marius George; Liu, Zhongming; Lotte, Fabien; Sud-Ouest, Inria Bordeaux; Meaney, LaBRI Paul; Mukkamala, Ramakrishna; Nikita, Konstantina S; Oelze, Michael L; Patton, James; Peterchev, Angel V; Penzel, Thomas; Rouse, Elliott J; Saha, Punam; Sakuma, Ichiro; Sawan, Mohamad; Schnabel, Julia; Segers, Patrick; Sejdić, Ervin; Shen, Dinggang; Staib, Lawrence; Stamoulis, Caterina; Stoyanov, Danail; Throckmorton, Amy L; Tian, Jie; Wang, May Dongmei; Wang, Yi; Woo, Eung Je; Yamamoto, Yoshi; Zeng, Fan-Gang; Zhang, Li-Qun; Zhang, Guoyan; Zhou, Ping; Bardakjian, Berj; Cauwenberghs, Gert; Chen, Wei; Dario, Paolo; Sant’Anna, Valdera Scuola Superiore; Fowlkes, J Brian; Gao, Shengkai; Henriquez, Craig; Lemieux, Louis; Newell, Jonathan C; Nie, Shuming; Sahakian, Alan V; Kooij, Herman Van Der; Wright, Steven; Zhang, Yuan-Ting
Amir Amini University of Louisville Fabio Babiloni University of Rome Journal Article
In: 2022.
@article{fichtinger0000o,
title = {Amir Amini University of Louisville Fabio Babiloni University of Rome},
author = {Walt Besio and Jovan Brankov and Sergio Cerutti and Ali Cinar and Yu Chen and Claudio Cobelli and Todd P Coleman and Rafael Davalos and Lei Ding and Dario Farina and Gabor Fichtinger and James Gee and Cuntai Guan and Kanado Harada and Ilko K Ilev and US Food and Pedro P Irazoqui and Jim Ji and Derek Kamper and Mehdi Kiani and Wen Li and Xin Li and Jie Liang and Marius George Linguraru and Zhongming Liu and Fabien Lotte and Inria Bordeaux Sud-Ouest and LaBRI Paul Meaney and Ramakrishna Mukkamala and Konstantina S Nikita and Michael L Oelze and James Patton and Angel V Peterchev and Thomas Penzel and Elliott J Rouse and Punam Saha and Ichiro Sakuma and Mohamad Sawan and Julia Schnabel and Patrick Segers and Ervin Sejdić and Dinggang Shen and Lawrence Staib and Caterina Stamoulis and Danail Stoyanov and Amy L Throckmorton and Jie Tian and May Dongmei Wang and Yi Wang and Eung Je Woo and Yoshi Yamamoto and Fan-Gang Zeng and Li-Qun Zhang and Guoyan Zhang and Ping Zhou and Berj Bardakjian and Gert Cauwenberghs and Wei Chen and Paolo Dario and Valdera Scuola Superiore Sant’Anna and J Brian Fowlkes and Shengkai Gao and Craig Henriquez and Louis Lemieux and Jonathan C Newell and Shuming Nie and Alan V Sahakian and Herman Van Der Kooij and Steven Wright and Yuan-Ting Zhang},
url = {https://ieeexplore.ieee.org/abstract/document/9737579/},
year = {2022},
date = {2022-04-01},
abstract = {Provides a listing of current committee members and society officers.},
keywords = {},
pubstate = {published},
tppubtype = {article}
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Provides a listing of current committee members and society officers.
Connolly, Laura; Jamzad, Amoon; Nikniazi, Arash; Poushimin, Rana; Nunzi, Jean Michel; Rudan, John; Fichtinger, Gabor; Mousavi, Parvin
Feasibility of combined optical and acoustic imaging for surgical cavity scanning Conference
SPIE Medical Imaging 2022: Image-Guided Procedures, Robotic Interventions, and Modeling, vol. 12034, San Diego (online), 2022.
@conference{Connolly2022,
title = {Feasibility of combined optical and acoustic imaging for surgical cavity scanning},
author = {Laura Connolly and Amoon Jamzad and Arash Nikniazi and Rana Poushimin and Jean Michel Nunzi and John Rudan and Gabor Fichtinger and Parvin Mousavi},
doi = {https://doi.org/10.1117/12.2611964},
year = {2022},
date = {2022-04-01},
booktitle = {SPIE Medical Imaging 2022: Image-Guided Procedures, Robotic Interventions, and Modeling},
volume = {12034},
address = {San Diego (online)},
abstract = {PURPOSE: Over 30% of breast conserving surgery patients must undergo repeat surgery to address incomplete tumor resection. We hypothesize that the addition of a robotic cavity scanning system can improve the success rates of these procedures by performing additional, intraoperative imaging to detect left-over cancer cells. In this study, we assess the feasibility of a combined optical and acoustic imaging approach for this cavity scanning system. METHODS: Dual-layer tissue phantoms are imaged with both throughput broadband spectroscopy and an endocavity ultrasound probe. The absorbance and transmittance of the incident light from the broadband source is used to characterize each tissue sample optically. Additionally, a temporally enhanced ultrasound approach is used to distinguish the heterogeneity of the tissue sample by classifying individual pixels in the ultrasound image with a support vector machine. The goal of this combined approach is to use optical characterization to classify the tissue surface, and acoustic characterization to classify the sample heterogeneity. RESULTS: Both optical and acoustic characterization demonstrated promising preliminary results. The class of each tissue sample is distinctly separable based on the transmittance and absorption of the broadband light. Additionally, an SVM trained on the temporally enhance ultrasound signals for each tissue type, showed 82% linear separability of labelled temporally enhanced ultrasound sequences in our test set. CONCLUSIONS: By combining broadband and ultrasound imaging, we demonstrate a potential non-destructive imaging approach for this robotic cavity scanning system. With this approach, our system can detect both surface level tissue characteristics and depth information. Applying this to breast conserving surgery can help inform the surgeon about the tissue composition of the resection cavity after initial tumor resection.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
PURPOSE: Over 30% of breast conserving surgery patients must undergo repeat surgery to address incomplete tumor resection. We hypothesize that the addition of a robotic cavity scanning system can improve the success rates of these procedures by performing additional, intraoperative imaging to detect left-over cancer cells. In this study, we assess the feasibility of a combined optical and acoustic imaging approach for this cavity scanning system. METHODS: Dual-layer tissue phantoms are imaged with both throughput broadband spectroscopy and an endocavity ultrasound probe. The absorbance and transmittance of the incident light from the broadband source is used to characterize each tissue sample optically. Additionally, a temporally enhanced ultrasound approach is used to distinguish the heterogeneity of the tissue sample by classifying individual pixels in the ultrasound image with a support vector machine. The goal of this combined approach is to use optical characterization to classify the tissue surface, and acoustic characterization to classify the sample heterogeneity. RESULTS: Both optical and acoustic characterization demonstrated promising preliminary results. The class of each tissue sample is distinctly separable based on the transmittance and absorption of the broadband light. Additionally, an SVM trained on the temporally enhance ultrasound signals for each tissue type, showed 82% linear separability of labelled temporally enhanced ultrasound sequences in our test set. CONCLUSIONS: By combining broadband and ultrasound imaging, we demonstrate a potential non-destructive imaging approach for this robotic cavity scanning system. With this approach, our system can detect both surface level tissue characteristics and depth information. Applying this to breast conserving surgery can help inform the surgeon about the tissue composition of the resection cavity after initial tumor resection.
Klosa, Elizabeth; Hisey, R.; Nazari, Tahmina; Wiggers, Theo; Zevin, Boris; Ungi, Tamas; Fichtinger, Gabor
Tissue segmentation for workflow recognition in open inguinal hernia repair training Conference
SPIE Medical Imaging, SPIE Medical Imaging SPIE Medical Imaging, San Diego, 2022.
@conference{Klosa2022a,
title = {Tissue segmentation for workflow recognition in open inguinal hernia repair training},
author = {Elizabeth Klosa and R. Hisey and Tahmina Nazari and Theo Wiggers and Boris Zevin and Tamas Ungi and Gabor Fichtinger},
url = {https://labs.cs.queensu.ca/perklab/wp-content/uploads/sites/3/2024/02/Klosa2022a.pdf},
year = {2022},
date = {2022-02-01},
urldate = {2022-02-01},
booktitle = {SPIE Medical Imaging},
publisher = {SPIE Medical Imaging},
address = {San Diego},
organization = {SPIE Medical Imaging},
abstract = {PURPOSE: As medical education adopts a competency-based training method, experts are spending substantial amounts of time instructing and assessing trainees’ competence. In this study, we look to develop a computer-assisted training platform that can provide instruction and assessment of open inguinal hernia repairs without needing an expert observer. We recognize workflow tasks based on the tool-tissue interactions, suggesting that we first need a method to identify tissues. This study aims to train a neural network in identifying tissues in a low-cost phantom as we work towards identifying the tool-tissue interactions needed for task recognition. METHODS: Eight simulated tissues were segmented throughout five videos from experienced surgeons who performed open inguinal hernia repairs on phantoms. A U-Net was trained using leave-one-user-out cross validation. The average F-score, false positive rate and false negative rate were calculated for each tissue to evaluate the U-Net’s performance. RESULTS: Higher F-scores and lower false negative and positive rates were recorded for the skin, hernia sac, spermatic cord, and nerves, while slightly lower metrics were recorded for the subcutaneous tissue, Scarpa’s fascia, external oblique aponeurosis and superficial epigastric vessels. CONCLUSION: The U-Net performed better in recognizing tissues that were relatively larger in size and more prevalent, while struggling to recognize smaller tissues only briefly visible. Since workflow recognition does not require perfect segmentation, we believe our U-Net is sufficient in recognizing the tissues of an inguinal hernia repair phantom. Future studies will explore combining our segmentation U-Net with tool detection as we work towards workflow recognition.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
PURPOSE: As medical education adopts a competency-based training method, experts are spending substantial amounts of time instructing and assessing trainees’ competence. In this study, we look to develop a computer-assisted training platform that can provide instruction and assessment of open inguinal hernia repairs without needing an expert observer. We recognize workflow tasks based on the tool-tissue interactions, suggesting that we first need a method to identify tissues. This study aims to train a neural network in identifying tissues in a low-cost phantom as we work towards identifying the tool-tissue interactions needed for task recognition. METHODS: Eight simulated tissues were segmented throughout five videos from experienced surgeons who performed open inguinal hernia repairs on phantoms. A U-Net was trained using leave-one-user-out cross validation. The average F-score, false positive rate and false negative rate were calculated for each tissue to evaluate the U-Net’s performance. RESULTS: Higher F-scores and lower false negative and positive rates were recorded for the skin, hernia sac, spermatic cord, and nerves, while slightly lower metrics were recorded for the subcutaneous tissue, Scarpa’s fascia, external oblique aponeurosis and superficial epigastric vessels. CONCLUSION: The U-Net performed better in recognizing tissues that were relatively larger in size and more prevalent, while struggling to recognize smaller tissues only briefly visible. Since workflow recognition does not require perfect segmentation, we believe our U-Net is sufficient in recognizing the tissues of an inguinal hernia repair phantom. Future studies will explore combining our segmentation U-Net with tool detection as we work towards workflow recognition.
Klosa, Elizabeth; Hisey, R.; Nazari, Tahmina; Wiggers, Theo; Zevin, Boris; Ungi, Tamas; Fichtinger, Gabor
Identifying tissues for task recognition in training of open inguinal hernia repairs Conference
Imaging Network of Ontario Symposium, 2022.
@conference{Klosa2022b,
title = {Identifying tissues for task recognition in training of open inguinal hernia repairs},
author = {Elizabeth Klosa and R. Hisey and Tahmina Nazari and Theo Wiggers and Boris Zevin and Tamas Ungi and Gabor Fichtinger},
url = {https://labs.cs.queensu.ca/perklab/wp-content/uploads/sites/3/2024/03/Klosa2022b.pdf},
year = {2022},
date = {2022-02-01},
urldate = {2022-02-01},
booktitle = {Imaging Network of Ontario Symposium},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
O’Driscoll, Olivia; Hisey, R.; Holden, M.; Camire, Daenis; Erb, Jason; Howes, Daniel; Ungi, Tamas; Fichtinger, Gabor
Feasibility of using object detection for performance assessment in central venous catherization Conference
Imaging Network of Ontario Symposium, 2022.
@conference{ODriscoll2022b,
title = {Feasibility of using object detection for performance assessment in central venous catherization},
author = {Olivia O’Driscoll and R. Hisey and M. Holden and Daenis Camire and Jason Erb and Daniel Howes and Tamas Ungi and Gabor Fichtinger},
url = {https://labs.cs.queensu.ca/perklab/wp-content/uploads/sites/3/2024/02/ODriscoll2021b.pdf},
year = {2022},
date = {2022-02-01},
urldate = {2022-02-01},
booktitle = {Imaging Network of Ontario Symposium},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
O’Driscoll, Olivia; Hisey, R.; Holden, M.; Camire, Daenis; Erb, Jason; Howes, Daniel; Ungi, Tamas; Fichtinger, Gabor
Feasibility of object detection for skill assessment in central venous catheterization Conference
SPIE Medical Imaging, SPIE Medical Imaging SPIE Medical Imaging, San Diego, 2022.
@conference{ODriscoll2022a,
title = {Feasibility of object detection for skill assessment in central venous catheterization},
author = {Olivia O’Driscoll and R. Hisey and M. Holden and Daenis Camire and Jason Erb and Daniel Howes and Tamas Ungi and Gabor Fichtinger},
url = {https://labs.cs.queensu.ca/perklab/wp-content/uploads/sites/3/2024/02/ODriscoll2022a.pdf},
year = {2022},
date = {2022-02-01},
urldate = {2022-02-01},
booktitle = {SPIE Medical Imaging},
publisher = {SPIE Medical Imaging},
address = {San Diego},
organization = {SPIE Medical Imaging},
abstract = {<p><strong>Purpose: </strong>Computer-assisted surgical skill assessment methods have traditionally relied on tracking tool motion with physical sensors. These tracking systems can be expensive, bulky, and impede tool function. Recent advances in object detection networks have made it possible to quantify tool motion using only a camera. These advances open the door for a low-cost alternative to current physical tracking systems for surgical skill assessment. This study determines the feasibility of using metrics computed with object detection by comparing them to widely accepted metrics computed using traditional tracking methods in central venous catheterization. <strong>Methods:</strong> Both video and tracking data were recorded from participants performing central venous catheterization on a venous access phantom. A Faster Region-Based Convolutional Neural Network was trained to recognize the ultrasound probe and syringe on the video data. Tracking-based metrics were computed using the Perk Tutor extension of 3D Slicer. The path length and usage time for each tool were then computed using both the video and tracking data. The metrics from object detection and tracking were compared using Spearman rank correlation. <strong>Results: </strong>The path lengths had a rank correlation coefficient of 0.22 for the syringe (p<0.03) and 0.35 (p<0.001) for the ultrasound probe. For the usage times, the correlation coefficient was 0.37 (p<0.001) for the syringe and 0.34 (p<0.001) for the ultrasound probe. <strong>Conclusions</strong>: The video-based metrics correlated significantly with the tracked metrics, suggesting that object detection could be a feasible skill assessment method for central venous catheterization.</p>},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
<p><strong>Purpose: </strong>Computer-assisted surgical skill assessment methods have traditionally relied on tracking tool motion with physical sensors. These tracking systems can be expensive, bulky, and impede tool function. Recent advances in object detection networks have made it possible to quantify tool motion using only a camera. These advances open the door for a low-cost alternative to current physical tracking systems for surgical skill assessment. This study determines the feasibility of using metrics computed with object detection by comparing them to widely accepted metrics computed using traditional tracking methods in central venous catheterization. <strong>Methods:</strong> Both video and tracking data were recorded from participants performing central venous catheterization on a venous access phantom. A Faster Region-Based Convolutional Neural Network was trained to recognize the ultrasound probe and syringe on the video data. Tracking-based metrics were computed using the Perk Tutor extension of 3D Slicer. The path length and usage time for each tool were then computed using both the video and tracking data. The metrics from object detection and tracking were compared using Spearman rank correlation. <strong>Results: </strong>The path lengths had a rank correlation coefficient of 0.22 for the syringe (p<0.03) and 0.35 (p<0.001) for the ultrasound probe. For the usage times, the correlation coefficient was 0.37 (p<0.001) for the syringe and 0.34 (p<0.001) for the ultrasound probe. <strong>Conclusions</strong>: The video-based metrics correlated significantly with the tracked metrics, suggesting that object detection could be a feasible skill assessment method for central venous catheterization.</p>
Kitner, Nicole; Rodgers, Jessica R.; Ungi, Tamas; Korzeniowski, Martin; Olding, Timothy; Joshi, C. P.; Mousavi, Parvin; Fichtinger, Gabor
Automated Catheter Segmentation in 3D Ultrasound Images from High-Dose-Rate Prostate Brachytherapy Conference
Imaging Network Ontario (IMNO) 2022 Symposium, Imaging Network of Ontario, Online, 2022.
@conference{Kitner2022ac,
title = {Automated Catheter Segmentation in 3D Ultrasound Images from High-Dose-Rate Prostate Brachytherapy},
author = {Nicole Kitner and Jessica R. Rodgers and Tamas Ungi and Martin Korzeniowski and Timothy Olding and C. P. Joshi and Parvin Mousavi and Gabor Fichtinger},
year = {2022},
date = {2022-02-01},
urldate = {2022-02-01},
booktitle = {Imaging Network Ontario (IMNO) 2022 Symposium},
publisher = {Imaging Network of Ontario},
address = {Online},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Austin, Catherine; Hisey, Rebecca; O'Driscoll, Olivia; Camire, Daenis; Erb, Jason; Howes, Daniel; Ungi, Tamas; Fichtinger, Gabor
Recognizing multiple needle insertion attempts for performance assessment in central venous catheterization training Journal Article
In: vol. 12034, pp. 518-524, 2022.
@article{fichtinger2022r,
title = {Recognizing multiple needle insertion attempts for performance assessment in central venous catheterization training},
author = {Catherine Austin and Rebecca Hisey and Olivia O'Driscoll and Daenis Camire and Jason Erb and Daniel Howes and Tamas Ungi and Gabor Fichtinger},
url = {https://www.spiedigitallibrary.org/conference-proceedings-of-spie/12034/1203428/Recognizing-multiple-needle-insertion-attempts-for-performance-assessment-in-central/10.1117/12.2613190.short},
year = {2022},
date = {2022-01-01},
volume = {12034},
pages = {518-524},
publisher = {SPIE},
abstract = {Purpose
Computer-assisted skill assessment has traditionally been focused on general metrics related to tool motion and usage time. While these metrics are important for an overall evaluation of skill, they do not address critical errors made during the procedure. This study examines the effectiveness of utilizing object detection to quantify the critical error of making multiple needle insertion attempts in central venous catheterization.
Methods
6860 images were annotated with ground truth bounding boxes around the syringe attached to the needle. The images were registered using the location of the phantom, and the bounding boxes from the training set were used to identify the regions where the needle was most likely inserting the phantom. A Faster region-based convolutional neural network was trained to identify the syringe and produce the bounding box location for images in the test set. A needle insertion …},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Purpose
Computer-assisted skill assessment has traditionally been focused on general metrics related to tool motion and usage time. While these metrics are important for an overall evaluation of skill, they do not address critical errors made during the procedure. This study examines the effectiveness of utilizing object detection to quantify the critical error of making multiple needle insertion attempts in central venous catheterization.
Methods
6860 images were annotated with ground truth bounding boxes around the syringe attached to the needle. The images were registered using the location of the phantom, and the bounding boxes from the training set were used to identify the regions where the needle was most likely inserting the phantom. A Faster region-based convolutional neural network was trained to identify the syringe and produce the bounding box location for images in the test set. A needle insertion …
Computer-assisted skill assessment has traditionally been focused on general metrics related to tool motion and usage time. While these metrics are important for an overall evaluation of skill, they do not address critical errors made during the procedure. This study examines the effectiveness of utilizing object detection to quantify the critical error of making multiple needle insertion attempts in central venous catheterization.
Methods
6860 images were annotated with ground truth bounding boxes around the syringe attached to the needle. The images were registered using the location of the phantom, and the bounding boxes from the training set were used to identify the regions where the needle was most likely inserting the phantom. A Faster region-based convolutional neural network was trained to identify the syringe and produce the bounding box location for images in the test set. A needle insertion …
Alqaoud, Motaz; Plemmons, John; Feliberti, Eric; Kaipa, Krishnanand; Dong, Siqin; Fichtinger, Gabor; Xiao, Yiming; Audette, Michel
Multi-Modality Breast MRI Segmentation Using NNU-NET For Preoperative Planning Of Robotic Surgery Navigation Journal Article
In: pp. 317-328, 2022.
@article{fichtinger2022q,
title = {Multi-Modality Breast MRI Segmentation Using NNU-NET For Preoperative Planning Of Robotic Surgery Navigation},
author = {Motaz Alqaoud and John Plemmons and Eric Feliberti and Krishnanand Kaipa and Siqin Dong and Gabor Fichtinger and Yiming Xiao and Michel Audette},
url = {https://ieeexplore.ieee.org/abstract/document/9859361/},
year = {2022},
date = {2022-01-01},
pages = {317-328},
publisher = {IEEE},
abstract = {Segmentation of the chest region and breast tissues is essential for surgery planning and navigation. This paper proposes the foundation for preoperative segmentation based on two cascaded architectures of deep neural networks (DNN) based on the state-of-the-art nnU-Net. Additionally, this study introduces a polyvinyl alcohol cryogel (PVA-C) breast phantom based on the segmentation of the DNN automated approach, enabling the experiments of navigation systems for robotic breast surgery. Multi-modality breast MRI datasets of T2W and STIR images were acquired from 10 patients. Segmentation evaluation utilized the Dice Similarity Coefficient (DSC), segmentation accuracy, sensitivity, and specificity. First, a single class labeling was used to segment the breast region. Then it was employed as an input for three-class labeling to segment fat, fibroglandular (FGT) tissues, and tumorous lesions. The first …},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Segmentation of the chest region and breast tissues is essential for surgery planning and navigation. This paper proposes the foundation for preoperative segmentation based on two cascaded architectures of deep neural networks (DNN) based on the state-of-the-art nnU-Net. Additionally, this study introduces a polyvinyl alcohol cryogel (PVA-C) breast phantom based on the segmentation of the DNN automated approach, enabling the experiments of navigation systems for robotic breast surgery. Multi-modality breast MRI datasets of T2W and STIR images were acquired from 10 patients. Segmentation evaluation utilized the Dice Similarity Coefficient (DSC), segmentation accuracy, sensitivity, and specificity. First, a single class labeling was used to segment the breast region. Then it was employed as an input for three-class labeling to segment fat, fibroglandular (FGT) tissues, and tumorous lesions. The first …
Kitner, Nicole; Rodgers, Jessica R; Ungi, Tamas; Korzeniowski, Martin; Olding, Tim; Joshi, Chandra P; Mousavi, Parvin; Fichtinger, Gabor
In: vol. 49, iss. 8, pp. 5662-5662, 2022.
@article{fichtinger2022p,
title = {Automatic catheter modelling in 3D transrectal ultrasound images from high-dose-rate prostate brachytherapy using a deep learning and feature extraction pipeline},
author = {Nicole Kitner and Jessica R Rodgers and Tamas Ungi and Martin Korzeniowski and Tim Olding and Chandra P Joshi and Parvin Mousavi and Gabor Fichtinger},
url = {https://scholar.google.com/scholar?cluster=1706675649324850852&hl=en&oi=scholarr},
year = {2022},
date = {2022-01-01},
volume = {49},
issue = {8},
pages = {5662-5662},
publisher = {WILEY},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Kitner, Nicole; Rodgers, Jessica R; Ungi, Tamas; Olding, Timothy; Joshi, Chandra; Mousavi, Parvin; Fichtinger, Gabor; Korzeniowski, Martin
49: Automated Catheter Tracking in 3D Ultrasound Images from High-Dose-Rate Prostate Brachytherapy Using Deep Learning and Feature Extraction Journal Article
In: Radiotherapy and Oncology, vol. 174, pp. S23-S24, 2022.
@article{fichtinger2022o,
title = {49: Automated Catheter Tracking in 3D Ultrasound Images from High-Dose-Rate Prostate Brachytherapy Using Deep Learning and Feature Extraction},
author = {Nicole Kitner and Jessica R Rodgers and Tamas Ungi and Timothy Olding and Chandra Joshi and Parvin Mousavi and Gabor Fichtinger and Martin Korzeniowski},
url = {https://scholar.google.com/scholar?cluster=2766988044564319338&hl=en&oi=scholarr},
year = {2022},
date = {2022-01-01},
journal = {Radiotherapy and Oncology},
volume = {174},
pages = {S23-S24},
publisher = {Elsevier},
abstract = {Purpose: Stereotactic body radiotherapy (SBRT) improves complete pain response for painful spinal metastases compared to conventional external beam radiotherapy (cEBRT). We report mature local control and reirradiation rates in a large cohort of patients treated with SBRT versus cEBRT enrolled previously in the Canadian Clinical Trials Group Symptom Control (SC). 24 Phase II/III trial.
Materials and Methods: 137/229 (60%) patients randomized to 24 Gy in 2 SBRT fractions or 20 Gy in 5 cEBRT fractions were retrospectively reviewed. By including all treated spinal segments, we report on 66 patients (119 spine segments) treated with SBRT, and 71 patients (169 segments) treated with cEBRT. The primary outcomes were MR-based local control and reirradiation rates for each treated spine segment.
Results: The median follow-up was 11.3 months (IQR: 5.3-27.7 months), and median OS in the SBRT and cEBRT …},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Purpose: Stereotactic body radiotherapy (SBRT) improves complete pain response for painful spinal metastases compared to conventional external beam radiotherapy (cEBRT). We report mature local control and reirradiation rates in a large cohort of patients treated with SBRT versus cEBRT enrolled previously in the Canadian Clinical Trials Group Symptom Control (SC). 24 Phase II/III trial.
Materials and Methods: 137/229 (60%) patients randomized to 24 Gy in 2 SBRT fractions or 20 Gy in 5 cEBRT fractions were retrospectively reviewed. By including all treated spinal segments, we report on 66 patients (119 spine segments) treated with SBRT, and 71 patients (169 segments) treated with cEBRT. The primary outcomes were MR-based local control and reirradiation rates for each treated spine segment.
Results: The median follow-up was 11.3 months (IQR: 5.3-27.7 months), and median OS in the SBRT and cEBRT …
Materials and Methods: 137/229 (60%) patients randomized to 24 Gy in 2 SBRT fractions or 20 Gy in 5 cEBRT fractions were retrospectively reviewed. By including all treated spinal segments, we report on 66 patients (119 spine segments) treated with SBRT, and 71 patients (169 segments) treated with cEBRT. The primary outcomes were MR-based local control and reirradiation rates for each treated spine segment.
Results: The median follow-up was 11.3 months (IQR: 5.3-27.7 months), and median OS in the SBRT and cEBRT …