Biography
Chris is a PhD student in the School of Computing under the supervision of Dr. Gabor Fichtinger and Dr. Parvin Mousavi. Chris’ research is in deep learning for medical image segmentation and developing a navigation system for breast cancer surgery.
Publications
Kim, Andrew S.; Yeung, Chris; Szabo, Robert; Sunderland, Kyle; Hisey, Rebecca; Morton, David; Kikinis, Ron; Diao, Babacar; Mousavi, Parvin; Ungi, Tamas; Fichtinger, Gabor
SPIE, 2024.
@proceedings{Kim2024,
title = {Percutaneous nephrostomy needle guidance using real-time 3D anatomical visualization with live ultrasound segmentation},
author = {Andrew S. Kim and Chris Yeung and Robert Szabo and Kyle Sunderland and Rebecca Hisey and David Morton and Ron Kikinis and Babacar Diao and Parvin Mousavi and Tamas Ungi and Gabor Fichtinger},
editor = {Maryam E. Rettmann and Jeffrey H. Siewerdsen},
doi = {10.1117/12.3006533},
year = {2024},
date = {2024-03-29},
urldate = {2024-03-29},
publisher = {SPIE},
abstract = {
PURPOSE: Percutaneous nephrostomy is a commonly performed procedure to drain urine to provide relief in patients with hydronephrosis. Conventional percutaneous nephrostomy needle guidance methods can be difficult, expensive, or not portable. We propose an open-source real-time 3D anatomical visualization aid for needle guidance with live ultrasound segmentation and 3D volume reconstruction using free, open-source software. METHODS: Basic hydronephrotic kidney phantoms were created, and recordings of these models were manually segmented and used to train a deep learning model that makes live segmentation predictions to perform live 3D volume reconstruction of the fluid-filled cavity. Participants performed 5 needle insertions with the visualization aid and 5 insertions with ultrasound needle guidance on a kidney phantom in randomized order, and these were recorded. Recordings of the trials were analyzed for needle tip distance to the center of the target calyx, needle insertion time, and success rate. Participants also completed a survey on their experience. RESULTS: Using the visualization aid showed significantly higher accuracy, while needle insertion time and success rate were not statistically significant at our sample size. Participants mostly responded positively to the visualization aid, and 80% found it easier to use than ultrasound needle guidance. CONCLUSION: We found that our visualization aid produced increased accuracy and an overall positive experience. We demonstrated that our system is functional and stable and believe that the workflow with this system can be applied to other procedures. This visualization aid system is effective on phantoms and is ready for translation with clinical data.},
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PURPOSE: Percutaneous nephrostomy is a commonly performed procedure to drain urine to provide relief in patients with hydronephrosis. Conventional percutaneous nephrostomy needle guidance methods can be difficult, expensive, or not portable. We propose an open-source real-time 3D anatomical visualization aid for needle guidance with live ultrasound segmentation and 3D volume reconstruction using free, open-source software. METHODS: Basic hydronephrotic kidney phantoms were created, and recordings of these models were manually segmented and used to train a deep learning model that makes live segmentation predictions to perform live 3D volume reconstruction of the fluid-filled cavity. Participants performed 5 needle insertions with the visualization aid and 5 insertions with ultrasound needle guidance on a kidney phantom in randomized order, and these were recorded. Recordings of the trials were analyzed for needle tip distance to the center of the target calyx, needle insertion time, and success rate. Participants also completed a survey on their experience. RESULTS: Using the visualization aid showed significantly higher accuracy, while needle insertion time and success rate were not statistically significant at our sample size. Participants mostly responded positively to the visualization aid, and 80% found it easier to use than ultrasound needle guidance. CONCLUSION: We found that our visualization aid produced increased accuracy and an overall positive experience. We demonstrated that our system is functional and stable and believe that the workflow with this system can be applied to other procedures. This visualization aid system is effective on phantoms and is ready for translation with clinical data.
Kaufmann, Martin; Jamzad, Amoon; Ungi, Tamas; Rodgers, Jessica R; Koster, Teaghan; Yeung, Chris; Ehrlich, Josh; Santilli, Alice; Asselin, Mark; Janssen, Natasja; McMullen, Julie; Solberg, Kathryn; Cheesman, Joanna; Carlo, Alessia Di; Ren, Kevin Yi Mi; Varma, Sonal; Merchant, Shaila; Engel, Cecil Jay; Walker, G Ross; Gallo, Andrea; Jabs, Doris; Mousavi, Parvin; Fichtinger, Gabor; Rudan, John F
Abstract PO2-23-07: Three-dimensional navigated mass spectrometry for intraoperative margin assessment during breast cancer surgery Journal Article
In: Cancer Research, vol. 84, iss. 9_Supplement, pp. PO2-23-07-PO2-23-07, 2024.
@article{fichtinger2024c,
title = {Abstract PO2-23-07: Three-dimensional navigated mass spectrometry for intraoperative margin assessment during breast cancer surgery},
author = {Martin Kaufmann and Amoon Jamzad and Tamas Ungi and Jessica R Rodgers and Teaghan Koster and Chris Yeung and Josh Ehrlich and Alice Santilli and Mark Asselin and Natasja Janssen and Julie McMullen and Kathryn Solberg and Joanna Cheesman and Alessia Di Carlo and Kevin Yi Mi Ren and Sonal Varma and Shaila Merchant and Cecil Jay Engel and G Ross Walker and Andrea Gallo and Doris Jabs and Parvin Mousavi and Gabor Fichtinger and John F Rudan},
url = {https://aacrjournals.org/cancerres/article/84/9_Supplement/PO2-23-07/743683},
year = {2024},
date = {2024-01-01},
journal = {Cancer Research},
volume = {84},
issue = {9_Supplement},
pages = {PO2-23-07-PO2-23-07},
publisher = {The American Association for Cancer Research},
abstract = {Positive resection margins occur in approximately 25% of breast cancer (BCa) surgeries, requiring re-operation. Margin status is not routinely available during surgery; thus, technologies that identify residual cancer on the specimen or cavity are needed to provide intraoperative decision support that may reduce positive margin rates. Rapid evaporative ionization mass spectrometry (REIMS) is an emerging technique that chemically profiles the plume generated by tissue cauterization to classify the ablated tissue as either cancerous or non-cancerous, on the basis of detected lipid species. Although REIMS can distinguish cancer and non-cancerous breast tissue by the signals generated, it does not indicate the location of the classified tissue in real-time. Our objective was to combine REIMS with spatio-temporal navigation (navigated REIMS), and to compare performance of navigated REIMS with conventional …},
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Positive resection margins occur in approximately 25% of breast cancer (BCa) surgeries, requiring re-operation. Margin status is not routinely available during surgery; thus, technologies that identify residual cancer on the specimen or cavity are needed to provide intraoperative decision support that may reduce positive margin rates. Rapid evaporative ionization mass spectrometry (REIMS) is an emerging technique that chemically profiles the plume generated by tissue cauterization to classify the ablated tissue as either cancerous or non-cancerous, on the basis of detected lipid species. Although REIMS can distinguish cancer and non-cancerous breast tissue by the signals generated, it does not indicate the location of the classified tissue in real-time. Our objective was to combine REIMS with spatio-temporal navigation (navigated REIMS), and to compare performance of navigated REIMS with conventional …
Yeung, Chris; Ungi, Tamas; Hu, Zoe; Jamzad, Amoon; Kaufmann, Martin; Walker, Ross; Merchant, Shaila; Engel, Cecil Jay; Jabs, Doris; Rudan, John; Mousavi, Parvin; Fichtinger, Gabor
From quantitative metrics to clinical success: assessing the utility of deep learning for tumor segmentation in breast surgery Journal Article
In: International Journal of Computer Assisted Radiology and Surgery, pp. 1-9, 2024.
@article{yeung2024,
title = {From quantitative metrics to clinical success: assessing the utility of deep learning for tumor segmentation in breast surgery},
author = {Chris Yeung and Tamas Ungi and Zoe Hu and Amoon Jamzad and Martin Kaufmann and Ross Walker and Shaila Merchant and Cecil Jay Engel and Doris Jabs and John Rudan and Parvin Mousavi and Gabor Fichtinger},
url = {https://link.springer.com/article/10.1007/s11548-024-03133-y},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {International Journal of Computer Assisted Radiology and Surgery},
pages = {1-9},
publisher = {Springer International Publishing},
abstract = {Purpose
Preventing positive margins is essential for ensuring favorable patient outcomes following breast-conserving surgery (BCS). Deep learning has the potential to enable this by automatically contouring the tumor and guiding resection in real time. However, evaluation of such models with respect to pathology outcomes is necessary for their successful translation into clinical practice.
Methods
Sixteen deep learning models based on established architectures in the literature are trained on 7318 ultrasound images from 33 patients. Models are ranked by an expert based on their contours generated from images in our test set. Generated contours from each model are also analyzed using recorded cautery trajectories of five navigated BCS cases to predict margin status. Predicted margins are compared with pathology reports.
Results
The best-performing model using both quantitative evaluation and our visual …},
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pubstate = {published},
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Purpose
Preventing positive margins is essential for ensuring favorable patient outcomes following breast-conserving surgery (BCS). Deep learning has the potential to enable this by automatically contouring the tumor and guiding resection in real time. However, evaluation of such models with respect to pathology outcomes is necessary for their successful translation into clinical practice.
Methods
Sixteen deep learning models based on established architectures in the literature are trained on 7318 ultrasound images from 33 patients. Models are ranked by an expert based on their contours generated from images in our test set. Generated contours from each model are also analyzed using recorded cautery trajectories of five navigated BCS cases to predict margin status. Predicted margins are compared with pathology reports.
Results
The best-performing model using both quantitative evaluation and our visual …
Preventing positive margins is essential for ensuring favorable patient outcomes following breast-conserving surgery (BCS). Deep learning has the potential to enable this by automatically contouring the tumor and guiding resection in real time. However, evaluation of such models with respect to pathology outcomes is necessary for their successful translation into clinical practice.
Methods
Sixteen deep learning models based on established architectures in the literature are trained on 7318 ultrasound images from 33 patients. Models are ranked by an expert based on their contours generated from images in our test set. Generated contours from each model are also analyzed using recorded cautery trajectories of five navigated BCS cases to predict margin status. Predicted margins are compared with pathology reports.
Results
The best-performing model using both quantitative evaluation and our visual …
Hashtrudi-Zaad, Kian; Ungi, Tamas; Yeung, Chris; Baum, Zachary; Cernelev, Pavel-Dumitru; Hage, Anthony N; Schlenger, Christopher; Fichtinger, Gabor
Expert-guided optimization of ultrasound segmentation models for 3D spine imaging Journal Article
In: pp. 680-685, 2024.
@article{hashtrudi-zaad2024,
title = {Expert-guided optimization of ultrasound segmentation models for 3D spine imaging},
author = {Kian Hashtrudi-Zaad and Tamas Ungi and Chris Yeung and Zachary Baum and Pavel-Dumitru Cernelev and Anthony N Hage and Christopher Schlenger and Gabor Fichtinger},
year = {2024},
date = {2024-01-01},
pages = {680-685},
publisher = {IEEE},
abstract = {We explored ultrasound for imaging bones, specifically the spine, as a safer and more accessible alternative to conventional X-ray. We aimed to improve how well deep learning segmentation models filter bone signals from ultrasound frames with the goal of using these segmented images for reconstructing the 3-dimensional spine volume.Our dataset consisted of spatially tracked ultrasound scans from 25 patients. Image frames from these scans were also manually annotated to provide training data for image segmentation deep learning. To find the optimal automatic segmentation method, we assessed five different artificial neural network models and their variations by hyperparameter tuning. Our main contribution is a new approach for model selection, employing an Elo rating system to efficiently rank trained models based on their visual performance as assessed by clinical users. This method addresses the …},
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We explored ultrasound for imaging bones, specifically the spine, as a safer and more accessible alternative to conventional X-ray. We aimed to improve how well deep learning segmentation models filter bone signals from ultrasound frames with the goal of using these segmented images for reconstructing the 3-dimensional spine volume.Our dataset consisted of spatially tracked ultrasound scans from 25 patients. Image frames from these scans were also manually annotated to provide training data for image segmentation deep learning. To find the optimal automatic segmentation method, we assessed five different artificial neural network models and their variations by hyperparameter tuning. Our main contribution is a new approach for model selection, employing an Elo rating system to efficiently rank trained models based on their visual performance as assessed by clinical users. This method addresses the …
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.},
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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.
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}
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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.
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 …},
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pubstate = {published},
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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 …
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},
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