Syeda, Ayesha; Fooladgar, Fahimeh; Jamzad, Amoon; Srikanthan, Dilakshan; Kaufmann, Martin; Ren, Kevin; Engel, Jay; Walker, Ross; Merchant, Shaila; McKay, Doug; Varma, Sonal; Fichtinger, Gabor; Rudan, John; Mousavi, Parvin
Self-supervised learning and uncertainty estimation for surgical margin detection Journal Article
In: vol. 12466, pp. 76-83, 2023.
@article{fichtinger2023r,
title = {Self-supervised learning and uncertainty estimation for surgical margin detection},
author = {Ayesha Syeda and Fahimeh Fooladgar and Amoon Jamzad and Dilakshan Srikanthan and Martin Kaufmann and Kevin Ren and Jay Engel and Ross Walker and Shaila Merchant and Doug McKay and Sonal Varma and Gabor Fichtinger and John Rudan and Parvin Mousavi},
url = {https://www.spiedigitallibrary.org/conference-proceedings-of-spie/12466/124660B/Self-supervised-learning-and-uncertainty-estimation-for-surgical-margin-detection/10.1117/12.2654104.short},
year = {2023},
date = {2023-01-01},
volume = {12466},
pages = {76-83},
publisher = {SPIE},
abstract = {Up to 35% of breast-conserving surgeries fail to resect all the tumors completely. Ideally, machine learning methods using the iKnife data, which uses Rapid Evaporative Ionization Mass Spectrometry (REIMS), can be utilized to predict tissue type in real-time during surgery, resulting in better tumor resections. As REIMS data is heterogeneous and weakly labeled, and datasets are often small, model performance and reliability can be adversely affected. Self-supervised training and uncertainty estimation of the prediction can be used to mitigate these challenges by learning the signatures of input data without their label as well as including predictive confidence in output reporting. We first design an autoencoder model using a reconstruction pretext task as a self-supervised pretraining step without considering tissue type. Next, we construct our uncertainty-aware classifier using the encoder part of the model with …},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Up to 35% of breast-conserving surgeries fail to resect all the tumors completely. Ideally, machine learning methods using the iKnife data, which uses Rapid Evaporative Ionization Mass Spectrometry (REIMS), can be utilized to predict tissue type in real-time during surgery, resulting in better tumor resections. As REIMS data is heterogeneous and weakly labeled, and datasets are often small, model performance and reliability can be adversely affected. Self-supervised training and uncertainty estimation of the prediction can be used to mitigate these challenges by learning the signatures of input data without their label as well as including predictive confidence in output reporting. We first design an autoencoder model using a reconstruction pretext task as a self-supervised pretraining step without considering tissue type. Next, we construct our uncertainty-aware classifier using the encoder part of the model with …
Yeung, Chris; Ehrlich, Joshua; Jamzad, Amoon; Kaufmann, Martin; Rudan, John; Engel, Cecil Jay; Mousavi, Parvin; Ungi, Tamas; Fichtinger, Gabor
Cautery trajectory analysis for evaluation of resection margins in breast-conserving surgery Journal Article
In: vol. 12466, pp. 495-501, 2023.
@article{fichtinger2023q,
title = {Cautery trajectory analysis for evaluation of resection margins in breast-conserving surgery},
author = {Chris Yeung and Joshua Ehrlich and Amoon Jamzad and Martin Kaufmann and John Rudan and Cecil Jay Engel and Parvin Mousavi and Tamas Ungi and Gabor Fichtinger},
url = {https://www.spiedigitallibrary.org/conference-proceedings-of-spie/12466/1246622/Cautery-trajectory-analysis-for-evaluation-of-resection-margins-in-breast/10.1117/12.2654497.short},
year = {2023},
date = {2023-01-01},
volume = {12466},
pages = {495-501},
publisher = {SPIE},
abstract = {After breast-conserving surgery, positive margins occur when breast cancer cells are found on the resection margin, leading to a higher chance of recurrence and the need for repeat surgery. The NaviKnife is an electromagnetic tracking-based surgical navigation system that helps to provide visual and spatial feedback to the surgeon. In this study, we conduct a gross evaluation of this navigation system with respect to resection margins. The trajectory of the surgical cautery relative to ultrasound-visible tumor will be visualized, and its distance and location from the tumor will be compared with pathology reports. Six breast-conserving surgery cases that resulted in positive margins were performed using the NaviKnife system. Trackers were placed on the surgical tools and their positions in three-dimensional space were recorded throughout the procedure. The closest distance between the cautery and the tumor …},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
After breast-conserving surgery, positive margins occur when breast cancer cells are found on the resection margin, leading to a higher chance of recurrence and the need for repeat surgery. The NaviKnife is an electromagnetic tracking-based surgical navigation system that helps to provide visual and spatial feedback to the surgeon. In this study, we conduct a gross evaluation of this navigation system with respect to resection margins. The trajectory of the surgical cautery relative to ultrasound-visible tumor will be visualized, and its distance and location from the tumor will be compared with pathology reports. Six breast-conserving surgery cases that resulted in positive margins were performed using the NaviKnife system. Trackers were placed on the surgical tools and their positions in three-dimensional space were recorded throughout the procedure. The closest distance between the cautery and the tumor …
Hashtrudi-Zaad, Kian; Hisey, Rebecca; Klosa, Elizabeth; Zevin, Boris; Ungi, Tamas; Fichtinger, Gabor
Using object detection for surgical tool recognition in simulated open inguinal hernia repair surgery Journal Article
In: vol. 12466, pp. 96-101, 2023.
@article{fichtinger2023p,
title = {Using object detection for surgical tool recognition in simulated open inguinal hernia repair surgery},
author = {Kian Hashtrudi-Zaad and Rebecca Hisey and Elizabeth Klosa and Boris Zevin and Tamas Ungi and Gabor Fichtinger},
url = {https://www.spiedigitallibrary.org/conference-proceedings-of-spie/12466/124660E/Using-object-detection-for-surgical-tool-recognition-in-simulated-open/10.1117/12.2654393.short},
year = {2023},
date = {2023-01-01},
volume = {12466},
pages = {96-101},
publisher = {SPIE},
abstract = {Following the shift from time-based medical education to a competency-based approach, a computer-assisted training platform would help relieve some of the new time burden placed on physicians. A vital component of these platforms is the computation of competency metrics which are based on surgical tool motion. Recognizing the class and motion of surgical tools is one step in the development of a training platform. Object detection can achieve tool recognition. While previous literature has reported on tool recognition in minimally invasive surgeries, open surgeries have not received the same attention. Open Inguinal Hernia Repair (OIHR), a common surgery that general surgery residents must learn, is an example of such surgeries. We present a method for object detection to recognize surgical tools in simulated OIHR. Images were extracted from six video recordings of OIHR performed on phantoms. Tools …},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Following the shift from time-based medical education to a competency-based approach, a computer-assisted training platform would help relieve some of the new time burden placed on physicians. A vital component of these platforms is the computation of competency metrics which are based on surgical tool motion. Recognizing the class and motion of surgical tools is one step in the development of a training platform. Object detection can achieve tool recognition. While previous literature has reported on tool recognition in minimally invasive surgeries, open surgeries have not received the same attention. Open Inguinal Hernia Repair (OIHR), a common surgery that general surgery residents must learn, is an example of such surgeries. We present a method for object detection to recognize surgical tools in simulated OIHR. Images were extracted from six video recordings of OIHR performed on phantoms. Tools …
March, L; Rodgers, JR; Hisey, R; Jamzad, A; Santilli, AML; McKay, D; Rudan, JF; Kaufmann, M; Ren, KYM; Fichtinger, G; Mousavi, P
Cautery tool state detection using deep learning on intraoperative surgery videos Journal Article
In: vol. 12466, pp. 89-95, 2023.
@article{fichtinger2023o,
title = {Cautery tool state detection using deep learning on intraoperative surgery videos},
author = {L March and JR Rodgers and R Hisey and A Jamzad and AML Santilli and D McKay and JF Rudan and M Kaufmann and KYM Ren and G Fichtinger and P Mousavi},
url = {https://www.spiedigitallibrary.org/conference-proceedings-of-spie/12466/124660D/Cautery-tool-state-detection-using-deep-learning-on-intraoperative-surgery/10.1117/12.2654234.short},
year = {2023},
date = {2023-01-01},
volume = {12466},
pages = {89-95},
publisher = {SPIE},
abstract = {Treatment for Basal Cell Carcinoma (BCC) includes an excisional surgery to remove cancerous tissues, using a cautery tool to make burns along a defined resection margin around the tumor. Margin evaluation occurs post-surgically, requiring repeat surgery if positive margins are detected. Rapid Evaporative Ionization Mass Spectrometry (REIMS) can help distinguish healthy and cancerous tissue but does not provide spatial information about the cautery tool location where the spectra are acquired. We propose using intraoperative surgical video recordings and deep learning to provide surgeons with guidance to locate sites of potential positive margins. Frames from 14 intraoperative videos of BCC surgery were extracted and used to train a sequence of networks. The first network extracts frames showing surgery in-progress, then, an object detection network localizes the cautery tool and resection margin. Finally …},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Treatment for Basal Cell Carcinoma (BCC) includes an excisional surgery to remove cancerous tissues, using a cautery tool to make burns along a defined resection margin around the tumor. Margin evaluation occurs post-surgically, requiring repeat surgery if positive margins are detected. Rapid Evaporative Ionization Mass Spectrometry (REIMS) can help distinguish healthy and cancerous tissue but does not provide spatial information about the cautery tool location where the spectra are acquired. We propose using intraoperative surgical video recordings and deep learning to provide surgeons with guidance to locate sites of potential positive margins. Frames from 14 intraoperative videos of BCC surgery were extracted and used to train a sequence of networks. The first network extracts frames showing surgery in-progress, then, an object detection network localizes the cautery tool and resection margin. Finally …
Alqaoud, Motaz; Plemmons, John; Feliberti, Eric; Kaipa, Krishnanand; Fichtinger, Gabor; Xiao, Yiming; Rashid, Tanweer; Audette, Michel
Multi-Material, Approach-Guided, Controlled-Resolution Breast Meshing for Fe-Based Interactive Surgery Simulation Journal Article
In: pp. 402-412, 2023.
@article{fichtinger2023n,
title = {Multi-Material, Approach-Guided, Controlled-Resolution Breast Meshing for Fe-Based Interactive Surgery Simulation},
author = {Motaz Alqaoud and John Plemmons and Eric Feliberti and Krishnanand Kaipa and Gabor Fichtinger and Yiming Xiao and Tanweer Rashid and Michel Audette},
url = {https://ieeexplore.ieee.org/abstract/document/10155366/},
year = {2023},
date = {2023-01-01},
pages = {402-412},
publisher = {IEEE},
abstract = {This paper proposes a guided, controlled resolution framework for 3D multi-material meshing. Using data from magnetic resonance (MR) images, we efficiently focused on demonstrating our framework for patient-specific breast cases. As a result, we can preserve the shared boundaries and enhance the resolution without negating the aspect of simulation computing time needed for finite element analysis (FEA). Our output is a high-quality volumetric mesh comprising 21K cells representing the three main parts for breast surgery simulation and planning, fat, fibroglandular (FGT), and tumor mass. Our approach combines three steps, surface meshing, surface mesh decimation, and generating a volumetric mesh. We showed experimental results for every stage and compared our final output to other literature, proving our method's efficiency in an accurate, simple, and high-quality presentation of a patient-specific …},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
This paper proposes a guided, controlled resolution framework for 3D multi-material meshing. Using data from magnetic resonance (MR) images, we efficiently focused on demonstrating our framework for patient-specific breast cases. As a result, we can preserve the shared boundaries and enhance the resolution without negating the aspect of simulation computing time needed for finite element analysis (FEA). Our output is a high-quality volumetric mesh comprising 21K cells representing the three main parts for breast surgery simulation and planning, fat, fibroglandular (FGT), and tumor mass. Our approach combines three steps, surface meshing, surface mesh decimation, and generating a volumetric mesh. We showed experimental results for every stage and compared our final output to other literature, proving our method's efficiency in an accurate, simple, and high-quality presentation of a patient-specific …
Groves, Leah A; Keita, Mohamed; Talla, Saidou; Kikinis, Ron; Fichtinger, Gabor; Mousavi, Parvin; Camara, Mamadou
A Review of Low-cost Ultrasound Compatible Phantoms Journal Article
In: 2023.
@article{fichtinger2023m,
title = {A Review of Low-cost Ultrasound Compatible Phantoms},
author = {Leah A Groves and Mohamed Keita and Saidou Talla and Ron Kikinis and Gabor Fichtinger and Parvin Mousavi and Mamadou Camara},
url = {https://ieeexplore.ieee.org/abstract/document/10157973/},
year = {2023},
date = {2023-01-01},
publisher = {IEEE},
abstract = {Ultrasound-compatible phantoms are used to develop novel US-based systems and train simulated medical interventions. The price difference between lab-made and commercially available ultrasound-compatible phantoms lead to the publication of many papers categorized as low-cost in the literature. The aim of this review was to improve the phantom selection process by summarizing the pertinent literature. We compiled papers on US-compatible spine, prostate, vascular, breast, kidney, and li ver phantoms. We reviewed papers for cost and accessibility, providing an overview of the materials, construction time, shelf life, needle insertion limits, and manufacturing and evaluation methods. This information was summarized by anatomy. The clinical application associated with each phantom was also reported for those interested in a particular intervention. Techniques and common practices for building low-cost …},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Ultrasound-compatible phantoms are used to develop novel US-based systems and train simulated medical interventions. The price difference between lab-made and commercially available ultrasound-compatible phantoms lead to the publication of many papers categorized as low-cost in the literature. The aim of this review was to improve the phantom selection process by summarizing the pertinent literature. We compiled papers on US-compatible spine, prostate, vascular, breast, kidney, and li ver phantoms. We reviewed papers for cost and accessibility, providing an overview of the materials, construction time, shelf life, needle insertion limits, and manufacturing and evaluation methods. This information was summarized by anatomy. The clinical application associated with each phantom was also reported for those interested in a particular intervention. Techniques and common practices for building low-cost …
Ndiaye, Fatou Bintou; Groves, Leah; Hisey, Rebecca; Ungi, Tamas; Diop, Idy; Mousavi, Parvin; Fichtinger, Gabor; Camara, Mamadou Samba
Desing and realization of a computer-assisted nephrostomy guidance system Journal Article
In: pp. 1-6, 2023.
@article{fichtinger2023l,
title = {Desing and realization of a computer-assisted nephrostomy guidance system},
author = {Fatou Bintou Ndiaye and Leah Groves and Rebecca Hisey and Tamas Ungi and Idy Diop and Parvin Mousavi and Gabor Fichtinger and Mamadou Samba Camara},
url = {https://ieeexplore.ieee.org/abstract/document/10253146/},
year = {2023},
date = {2023-01-01},
pages = {1-6},
publisher = {IEEE},
abstract = {Background and purpose
Nowadays, computerized nephrostomy techniques exist. Although relatively safe, several factors make it difficult for inexperienced users. A computer-assisted nephrostomy guidance system has been studied to increase the success rate of this intervention and reduce the work and difficulties encountered by the actors.
Methods
To design the system, two methods will be studied. Following this study, this system was designed based on method 2. SmartSysNephro is composed of a hardware part whose manipulations made by the user are visualized and assisted by the computer. This nephrostomy procedure that the user simulates is monitored by webcam. Using the data from this Intel Real Sense webcam, allowed to propose a CNN YOLO model.
Results
The results obtained show that the objectives set have been achieved globally. The SmartSysNephro system gives real time warning …},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Background and purpose
Nowadays, computerized nephrostomy techniques exist. Although relatively safe, several factors make it difficult for inexperienced users. A computer-assisted nephrostomy guidance system has been studied to increase the success rate of this intervention and reduce the work and difficulties encountered by the actors.
Methods
To design the system, two methods will be studied. Following this study, this system was designed based on method 2. SmartSysNephro is composed of a hardware part whose manipulations made by the user are visualized and assisted by the computer. This nephrostomy procedure that the user simulates is monitored by webcam. Using the data from this Intel Real Sense webcam, allowed to propose a CNN YOLO model.
Results
The results obtained show that the objectives set have been achieved globally. The SmartSysNephro system gives real time warning …
Nowadays, computerized nephrostomy techniques exist. Although relatively safe, several factors make it difficult for inexperienced users. A computer-assisted nephrostomy guidance system has been studied to increase the success rate of this intervention and reduce the work and difficulties encountered by the actors.
Methods
To design the system, two methods will be studied. Following this study, this system was designed based on method 2. SmartSysNephro is composed of a hardware part whose manipulations made by the user are visualized and assisted by the computer. This nephrostomy procedure that the user simulates is monitored by webcam. Using the data from this Intel Real Sense webcam, allowed to propose a CNN YOLO model.
Results
The results obtained show that the objectives set have been achieved globally. The SmartSysNephro system gives real time warning …
Fichtinger, Gabor; Holden, Matthew; Jannin, Pierre; Haegelen, Claire; Zhao, Yulong
Proficiency assessment system and method for deep brain stimulation (DBS) Journal Article
In: 2023.
@article{fichtinger2023k,
title = {Proficiency assessment system and method for deep brain stimulation (DBS)},
author = {Gabor Fichtinger and Matthew Holden and Pierre Jannin and Claire Haegelen and Yulong Zhao},
url = {https://patents.google.com/patent/US11756689B2/en},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
abstract = {A method for simulating a deep-brain stimulation in a computer-assisted platform that includes providing to a neurosurgeon, through a man-machine interface, visual information of a pre-operative situation, including a representation of a brain. The method also includes monitoring inputs of said neurosurgeon on the man-machine interface, until a trajectory is determined between an entry point and a target for the placement of an electrode. The method further includes comparing said trajectory to a set of previously-established trajectories for the pre-operative situation, so as to determine an overall measurement representative of a quality of the trajectory compared to the previously-established trajectories.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
A method for simulating a deep-brain stimulation in a computer-assisted platform that includes providing to a neurosurgeon, through a man-machine interface, visual information of a pre-operative situation, including a representation of a brain. The method also includes monitoring inputs of said neurosurgeon on the man-machine interface, until a trajectory is determined between an entry point and a target for the placement of an electrode. The method further includes comparing said trajectory to a set of previously-established trajectories for the pre-operative situation, so as to determine an overall measurement representative of a quality of the trajectory compared to the previously-established trajectories.
Tomalty, Diane; Giovannetti, Olivia; Velikonja, Leah; Munday, Jasica; Kaufmann, Martin; Iaboni, Natasha; Jamzad, Amoon; Rubino, Rachel; Fichtinger, Gabor; Mousavi, Parvin; Nicol, Christopher JB; Rudan, John F; Adams, Michael A
Molecular characterization of human peripheral nerves using desorption electrospray ionization mass spectrometry imaging Journal Article
In: Journal of Anatomy, vol. 243, iss. 5, pp. 758-769, 2023.
@article{fichtinger2023j,
title = {Molecular characterization of human peripheral nerves using desorption electrospray ionization mass spectrometry imaging},
author = {Diane Tomalty and Olivia Giovannetti and Leah Velikonja and Jasica Munday and Martin Kaufmann and Natasha Iaboni and Amoon Jamzad and Rachel Rubino and Gabor Fichtinger and Parvin Mousavi and Christopher JB Nicol and John F Rudan and Michael A Adams},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/joa.13909},
year = {2023},
date = {2023-01-01},
journal = {Journal of Anatomy},
volume = {243},
issue = {5},
pages = {758-769},
abstract = {Desorption electrospray ionization mass spectrometry imaging (DESI‐MSI) is a molecular imaging method that can be used to elucidate the small‐molecule composition of tissues and map their spatial information using two‐dimensional ion images. This technique has been used to investigate the molecular profiles of variety of tissues, including within the central nervous system, specifically the brain and spinal cord. To our knowledge, this technique has yet to be applied to tissues of the peripheral nervous system (PNS). Data generated from such analyses are expected to advance the characterization of these structures. The study aimed to: (i) establish whether DESI‐MSI can discriminate the molecular characteristics of peripheral nerves and distinguish them from surrounding tissues and (ii) assess whether different peripheral nerve subtypes are characterized by unique molecular profiles. Four different nerves for …},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Desorption electrospray ionization mass spectrometry imaging (DESI‐MSI) is a molecular imaging method that can be used to elucidate the small‐molecule composition of tissues and map their spatial information using two‐dimensional ion images. This technique has been used to investigate the molecular profiles of variety of tissues, including within the central nervous system, specifically the brain and spinal cord. To our knowledge, this technique has yet to be applied to tissues of the peripheral nervous system (PNS). Data generated from such analyses are expected to advance the characterization of these structures. The study aimed to: (i) establish whether DESI‐MSI can discriminate the molecular characteristics of peripheral nerves and distinguish them from surrounding tissues and (ii) assess whether different peripheral nerve subtypes are characterized by unique molecular profiles. Four different nerves for …
Srikanthan, Dilakshan; Kaufmann, Martin; Jamzad, Amoon; Syeda, Ayesha; Santilli, Alice; Sedghi, Alireza; Fichtinger, Gabor; Purzner, Jamie; Rudan, John; Purzner, Teresa; Mousavi, Parvin
Attention-based multi-instance learning for improved glioblastoma detection using mass spectrometry Journal Article
In: vol. 12466, pp. 248-253, 2023.
@article{fichtinger2023i,
title = {Attention-based multi-instance learning for improved glioblastoma detection using mass spectrometry},
author = {Dilakshan Srikanthan and Martin Kaufmann and Amoon Jamzad and Ayesha Syeda and Alice Santilli and Alireza Sedghi and Gabor Fichtinger and Jamie Purzner and John Rudan and Teresa Purzner and Parvin Mousavi},
url = {https://www.spiedigitallibrary.org/conference-proceedings-of-spie/12466/1246611/Attention-based-multi-instance-learning-for-improved-glioblastoma-detection-using/10.1117/12.2654436.short},
year = {2023},
date = {2023-01-01},
volume = {12466},
pages = {248-253},
publisher = {SPIE},
abstract = {Glioblastoma Multiforme (GBM) is the most common and most lethal primary brain tumor in adults with a five-year survival rate of 5%. The current standard of care and survival rate have remained largely unchanged due to the degree of difficulty in surgically removing these tumors, which plays a crucial role in survival, as better surgical resection leads to longer survival times. Thus, novel technologies need to be identified to improve resection accuracy. Our study features a curated database of GBM and normal brain tissue specimens, which we used to train and validate a multi-instance learning model for GBM detection via rapid evaporative ionization mass spectrometry. This method enables real-time tissue typing. The specimens were collected by a surgeon, reviewed by a pathologist, and sampled with an electrocautery device. The dataset comprised 276 normal tissue burns and 321 GBM tissue burns. Our multi …},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Glioblastoma Multiforme (GBM) is the most common and most lethal primary brain tumor in adults with a five-year survival rate of 5%. The current standard of care and survival rate have remained largely unchanged due to the degree of difficulty in surgically removing these tumors, which plays a crucial role in survival, as better surgical resection leads to longer survival times. Thus, novel technologies need to be identified to improve resection accuracy. Our study features a curated database of GBM and normal brain tissue specimens, which we used to train and validate a multi-instance learning model for GBM detection via rapid evaporative ionization mass spectrometry. This method enables real-time tissue typing. The specimens were collected by a surgeon, reviewed by a pathologist, and sampled with an electrocautery device. The dataset comprised 276 normal tissue burns and 321 GBM tissue burns. Our multi …
Szabó, Róbert Zsolt; Orosz, Gábor; Ungi, Tamás; Barr, Colton; Yeung, Chris; Incze, Roland; Fichtinger, Gabor; Gál, János; Haidegger, Tamás
Automation of lung ultrasound imaging and image processing for bedside diagnostic examinations Journal Article
In: pp. 000779-000784, 2023.
@article{fichtinger2023h,
title = {Automation of lung ultrasound imaging and image processing for bedside diagnostic examinations},
author = {Róbert Zsolt Szabó and Gábor Orosz and Tamás Ungi and Colton Barr and Chris Yeung and Roland Incze and Gabor Fichtinger and János Gál and Tamás Haidegger},
url = {https://ieeexplore.ieee.org/abstract/document/10158672/},
year = {2023},
date = {2023-01-01},
pages = {000779-000784},
publisher = {IEEE},
abstract = {The causes of acute respiratory failure can be difficult to identify for physicians. Experts can differentiate these causes using bedside lung ultrasound, but lung ultrasound has a considerable learning curve. We investigate if an automated decision-support system could help novices interpret lung ultrasound scans. The system utilizes medical ultrasound, data processing, and a neural network implementation to achieve this goal. The article details the steps taken in the data preparation, and the implementation of the neural network. The best model’s accuracy and error rate are presented, along with examples of its predictions. The paper concludes with an evaluation of the results, identification of limitations, and suggestions for future improvements.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The causes of acute respiratory failure can be difficult to identify for physicians. Experts can differentiate these causes using bedside lung ultrasound, but lung ultrasound has a considerable learning curve. We investigate if an automated decision-support system could help novices interpret lung ultrasound scans. The system utilizes medical ultrasound, data processing, and a neural network implementation to achieve this goal. The article details the steps taken in the data preparation, and the implementation of the neural network. The best model’s accuracy and error rate are presented, along with examples of its predictions. The paper concludes with an evaluation of the results, identification of limitations, and suggestions for future improvements.
Jamzad, Amoon; Fooladgar, Fahimeh; Connolly, Laura; Srikanthan, Dilakshan; Syeda, Ayesha; Kaufmann, Martin; Ren, Kevin YM; Merchant, Shaila; Engel, Jay; Varma, Sonal; Fichtinger, Gabor; Rudan, John F; Mousavi, Parvin
Bridging Ex-Vivo Training and Intra-operative Deployment for Surgical Margin Assessment with Evidential Graph Transformer Journal Article
In: pp. 562-571, 2023.
@article{fichtinger2023g,
title = {Bridging Ex-Vivo Training and Intra-operative Deployment for Surgical Margin Assessment with Evidential Graph Transformer},
author = {Amoon Jamzad and Fahimeh Fooladgar and Laura Connolly and Dilakshan Srikanthan and Ayesha Syeda and Martin Kaufmann and Kevin YM Ren and Shaila Merchant and Jay Engel and Sonal Varma and Gabor Fichtinger and John F Rudan and Parvin Mousavi},
url = {https://link.springer.com/chapter/10.1007/978-3-031-43990-2_53},
year = {2023},
date = {2023-01-01},
pages = {562-571},
publisher = {Springer Nature Switzerland},
abstract = {PURPOSE
The use of intra-operative mass spectrometry along with Graph Transformer models showed promising results for margin detection on ex-vivo data. Although highly interpretable, these methods lack the ability to handle the uncertainty associated with intra-operative decision making. In this paper for the first time, we propose Evidential Graph Transformer network, a combination of attention mapping and uncertainty estimation to increase the performance and interpretability of surgical margin assessment.
METHODS
The Evidential Graph Transformer was formulated to output the uncertainty estimation along with intermediate attentions. The performance of the model was compared with different baselines in an ex-vivo cross-validation scheme, with extensive ablation study. The association of the model with clinical features were explored. The model was further validated for a prospective ex-vivo data, as …},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
PURPOSE
The use of intra-operative mass spectrometry along with Graph Transformer models showed promising results for margin detection on ex-vivo data. Although highly interpretable, these methods lack the ability to handle the uncertainty associated with intra-operative decision making. In this paper for the first time, we propose Evidential Graph Transformer network, a combination of attention mapping and uncertainty estimation to increase the performance and interpretability of surgical margin assessment.
METHODS
The Evidential Graph Transformer was formulated to output the uncertainty estimation along with intermediate attentions. The performance of the model was compared with different baselines in an ex-vivo cross-validation scheme, with extensive ablation study. The association of the model with clinical features were explored. The model was further validated for a prospective ex-vivo data, as …
The use of intra-operative mass spectrometry along with Graph Transformer models showed promising results for margin detection on ex-vivo data. Although highly interpretable, these methods lack the ability to handle the uncertainty associated with intra-operative decision making. In this paper for the first time, we propose Evidential Graph Transformer network, a combination of attention mapping and uncertainty estimation to increase the performance and interpretability of surgical margin assessment.
METHODS
The Evidential Graph Transformer was formulated to output the uncertainty estimation along with intermediate attentions. The performance of the model was compared with different baselines in an ex-vivo cross-validation scheme, with extensive ablation study. The association of the model with clinical features were explored. The model was further validated for a prospective ex-vivo data, as …
Pose-Díez-de-la-Lastra, Alicia; Ungi, Tamas; Morton, David; Fichtinger, Gabor; Pascau, Javier
Real-time integration between Microsoft HoloLens 2 and 3D Slicer with demonstration in pedicle screw placement planning Journal Article
In: International Journal of Computer Assisted Radiology and Surgery, vol. 18, iss. 11, pp. 2023-2032, 2023.
@article{fichtinger2023f,
title = {Real-time integration between Microsoft HoloLens 2 and 3D Slicer with demonstration in pedicle screw placement planning},
author = {Alicia Pose-Díez-de-la-Lastra and Tamas Ungi and David Morton and Gabor Fichtinger and Javier Pascau},
url = {https://link.springer.com/article/10.1007/s11548-023-02977-0},
year = {2023},
date = {2023-01-01},
journal = {International Journal of Computer Assisted Radiology and Surgery},
volume = {18},
issue = {11},
pages = {2023-2032},
publisher = {Springer International Publishing},
abstract = {Purpose
Up to date, there has been a lack of software infrastructure to connect 3D Slicer to any augmented reality (AR) device. This work describes a novel connection approach using Microsoft HoloLens 2 and OpenIGTLink, with a demonstration in pedicle screw placement planning.
Methods
We developed an AR application in Unity that is wirelessly rendered onto Microsoft HoloLens 2 using Holographic Remoting. Simultaneously, Unity connects to 3D Slicer using the OpenIGTLink communication protocol. Geometrical transform and image messages are transferred between both platforms in real time. Through the AR glasses, a user visualizes a patient’s computed tomography overlaid onto virtual 3D models showing anatomical structures. We technically evaluated the system by measuring message transference latency between the platforms. Its functionality was assessed in pedicle screw placement planning …},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Purpose
Up to date, there has been a lack of software infrastructure to connect 3D Slicer to any augmented reality (AR) device. This work describes a novel connection approach using Microsoft HoloLens 2 and OpenIGTLink, with a demonstration in pedicle screw placement planning.
Methods
We developed an AR application in Unity that is wirelessly rendered onto Microsoft HoloLens 2 using Holographic Remoting. Simultaneously, Unity connects to 3D Slicer using the OpenIGTLink communication protocol. Geometrical transform and image messages are transferred between both platforms in real time. Through the AR glasses, a user visualizes a patient’s computed tomography overlaid onto virtual 3D models showing anatomical structures. We technically evaluated the system by measuring message transference latency between the platforms. Its functionality was assessed in pedicle screw placement planning …
Up to date, there has been a lack of software infrastructure to connect 3D Slicer to any augmented reality (AR) device. This work describes a novel connection approach using Microsoft HoloLens 2 and OpenIGTLink, with a demonstration in pedicle screw placement planning.
Methods
We developed an AR application in Unity that is wirelessly rendered onto Microsoft HoloLens 2 using Holographic Remoting. Simultaneously, Unity connects to 3D Slicer using the OpenIGTLink communication protocol. Geometrical transform and image messages are transferred between both platforms in real time. Through the AR glasses, a user visualizes a patient’s computed tomography overlaid onto virtual 3D models showing anatomical structures. We technically evaluated the system by measuring message transference latency between the platforms. Its functionality was assessed in pedicle screw placement planning …
Whyne, Cari M; Underwood, Grace; Davidson, Sean RH; Robert, Normand; Huang, Christine; Akens, Margarete K; Fichtinger, Gabor; Yee, Albert JM; Hardisty, Michael
Development and validation of a radiofrequency ablation treatment planning system for vertebral metastases Journal Article
In: International Journal of Computer Assisted Radiology and Surgery, vol. 18, iss. 12, pp. 2339-2347, 2023.
@article{fichtinger2023e,
title = {Development and validation of a radiofrequency ablation treatment planning system for vertebral metastases},
author = {Cari M Whyne and Grace Underwood and Sean RH Davidson and Normand Robert and Christine Huang and Margarete K Akens and Gabor Fichtinger and Albert JM Yee and Michael Hardisty},
url = {https://link.springer.com/article/10.1007/s11548-023-02952-9},
year = {2023},
date = {2023-01-01},
journal = {International Journal of Computer Assisted Radiology and Surgery},
volume = {18},
issue = {12},
pages = {2339-2347},
publisher = {Springer International Publishing},
abstract = {Purpose
Bone-targeted radiofrequency ablation (RFA) is widely used in the treatment of vertebral metastases. While radiation therapy utilizes established treatment planning systems (TPS) based on multimodal imaging to optimize treatment volumes, current RFA of vertebral metastases has been limited to qualitative image-based assessment of tumour location to direct probe selection and access. This study aimed to design, develop and evaluate a computational patient-specific RFA TPS for vertebral metastases.
Methods
A TPS was developed on the open-source 3D slicer platform, including procedural setup, dose calculation (based on finite element modelling), and analysis/visualization modules. Usability testing was carried out by 7 clinicians involved in the treatment of vertebral metastases on retrospective clinical imaging data using a simplified dose calculation engine. In vivo evaluation was performed in a …},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Purpose
Bone-targeted radiofrequency ablation (RFA) is widely used in the treatment of vertebral metastases. While radiation therapy utilizes established treatment planning systems (TPS) based on multimodal imaging to optimize treatment volumes, current RFA of vertebral metastases has been limited to qualitative image-based assessment of tumour location to direct probe selection and access. This study aimed to design, develop and evaluate a computational patient-specific RFA TPS for vertebral metastases.
Methods
A TPS was developed on the open-source 3D slicer platform, including procedural setup, dose calculation (based on finite element modelling), and analysis/visualization modules. Usability testing was carried out by 7 clinicians involved in the treatment of vertebral metastases on retrospective clinical imaging data using a simplified dose calculation engine. In vivo evaluation was performed in a …
Bone-targeted radiofrequency ablation (RFA) is widely used in the treatment of vertebral metastases. While radiation therapy utilizes established treatment planning systems (TPS) based on multimodal imaging to optimize treatment volumes, current RFA of vertebral metastases has been limited to qualitative image-based assessment of tumour location to direct probe selection and access. This study aimed to design, develop and evaluate a computational patient-specific RFA TPS for vertebral metastases.
Methods
A TPS was developed on the open-source 3D slicer platform, including procedural setup, dose calculation (based on finite element modelling), and analysis/visualization modules. Usability testing was carried out by 7 clinicians involved in the treatment of vertebral metastases on retrospective clinical imaging data using a simplified dose calculation engine. In vivo evaluation was performed in a …
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}
}