{"id":2126,"date":"2024-08-25T20:54:09","date_gmt":"2024-08-25T20:54:09","guid":{"rendered":"https:\/\/labs.cs.queensu.ca\/perklab\/?post_type=qsc_member&p=2126"},"modified":"2024-08-25T20:54:10","modified_gmt":"2024-08-25T20:54:10","slug":"chris-yeung","status":"publish","type":"qsc_member","link":"https:\/\/labs.cs.queensu.ca\/perklab\/members\/chris-yeung\/","title":{"rendered":"Chris Yeung"},"content":{"rendered":"
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Chris Yeung<\/h1><\/div>\n\t\t
PhD Student<\/div>\n\t\t
School of Computing<\/div>\n\t\t
Queen’s University<\/div>\n\t\t
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chris.yeung@queensu.ca<\/a><\/div>\n\t\t\t
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Biography<\/h2>\n\n\n\n

Chris is a PhD student in the School of Computing under the supervision of Dr. Gabor Fichtinger and Dr. Parvin Mousavi. Chris\u2019 research is in deep learning for medical image segmentation and developing a navigation system for breast cancer surgery.<\/p>\n\n\n\n

Publications<\/h2>\n\n\n
<\/a><\/form>

Othman, Amira; Kaufmann, Martin; Koster, Teaghan; Jamzad, Amoon; Ungi, Tamas; Rodgers, Jessica; Mcmullen, Julie; Yeung, Chris; Janssen, Natasja; Solberg, Kathryn; Cheesman, Joanna; Rudan, John; Mousavi, Parvin; Fichtinger, Gabor; Hoyos, Andrea Gallo; Jabs, Doris; Engel, Jay; Merchant, Shaila; Walker, Ross; Ren, Kevin; Varma, Sonal<\/p>

211 Three-Dimensional Navigated Mass Spectrometry for Intraoperative Margin Assessment During Breast Cancer Surgery Journal Article<\/span> <\/p>

In: <\/span>Laboratory Investigation, <\/span>vol. 105, <\/span>no. 3, <\/span>2025<\/span>.<\/p>

Abstract<\/a><\/span> | BibTeX<\/a><\/span><\/p>

@article{othman2025,
\r\ntitle = {211 Three-Dimensional Navigated Mass Spectrometry for Intraoperative Margin Assessment During Breast Cancer Surgery},
\r\nauthor = {Amira Othman and Martin Kaufmann and Teaghan Koster and Amoon Jamzad and Tamas Ungi and Jessica Rodgers and Julie Mcmullen and Chris Yeung and Natasja Janssen and Kathryn Solberg and Joanna Cheesman and John Rudan and Parvin Mousavi and Gabor Fichtinger and Andrea Gallo Hoyos and Doris Jabs and Jay Engel and Shaila Merchant and Ross Walker and Kevin Ren and Sonal Varma},
\r\nyear  = {2025},
\r\ndate = {2025-01-01},
\r\njournal = {Laboratory Investigation},
\r\nvolume = {105},
\r\nnumber = {3},
\r\npublisher = {Elsevier},
\r\nabstract = {Background 
\r\nIntraoperative frozen sections are not routine in Breast cancer (BC), hence, patients with positive margin need reoperation for margin clearance. Technologies that can identify residual cancer in real-time during the surgery can be of immense help in reducing the morbidity, healthcare utilization and, the prognosis in BC. Rapid evaporative ionization mass spectrometry (REIMS) is a mass spectrometric technique that can chemically profile the surgical cauterization plume to classify the tissue as either cancerous, suspicious or non-cancerous. A plastic tube with solvent is attached to the cautery knife (i-knife) and it passes the smoke generated from cautery of the tissue to the mass spectrometric machine located in the OR. The spectra generated from this smoke solution are assessed in real-time to help classify the tissue. Our goal was to compare the accuracy of REIMS with histology (the gold standard) to \u2026},
\r\nkeywords = {},
\r\npubstate = {published},
\r\ntppubtype = {article}
\r\n}
\r\n<\/pre><\/div>
Close<\/a><\/p><\/div>
Background 
\r\nIntraoperative frozen sections are not routine in Breast cancer (BC), hence, patients with positive margin need reoperation for margin clearance. Technologies that can identify residual cancer in real-time during the surgery can be of immense help in reducing the morbidity, healthcare utilization and, the prognosis in BC. Rapid evaporative ionization mass spectrometry (REIMS) is a mass spectrometric technique that can chemically profile the surgical cauterization plume to classify the tissue as either cancerous, suspicious or non-cancerous. A plastic tube with solvent is attached to the cautery knife (i-knife) and it passes the smoke generated from cautery of the tissue to the mass spectrometric machine located in the OR. The spectra generated from this smoke solution are assessed in real-time to help classify the tissue. Our goal was to compare the accuracy of REIMS with histology (the gold standard) to \u2026<\/div>
Close<\/a><\/p><\/div><\/div><\/div>
 Kim, Andrew S.;  Yeung, Chris;  Szabo, Robert;  Sunderland, Kyle;  Hisey, Rebecca;  Morton, David;  Kikinis, Ron;  Diao, Babacar;  Mousavi, Parvin;  Ungi, Tamas;  Fichtinger, Gabor<\/p>
Percutaneous nephrostomy needle guidance using real-time 3D anatomical visualization with live ultrasound segmentation<\/a> Proceedings<\/span> <\/p>
SPIE, <\/span>2024<\/span>.<\/p>
Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>
@proceedings{Kim2024,
\r\ntitle = {Percutaneous nephrostomy needle guidance using real-time 3D anatomical visualization with live ultrasound segmentation},
\r\nauthor = {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},
\r\neditor = {Maryam E. Rettmann and Jeffrey H. Siewerdsen},
\r\ndoi = {10.1117\/12.3006533},
\r\nyear  = {2024},
\r\ndate = {2024-03-29},
\r\nurldate = {2024-03-29},
\r\npublisher = {SPIE},
\r\nabstract = {
\r\nPURPOSE: 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.},
\r\nkeywords = {},
\r\npubstate = {published},
\r\ntppubtype = {proceedings}
\r\n}
\r\n<\/pre><\/div>
Close<\/a><\/p><\/div>

\r\nPURPOSE: 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.<\/div>
Close<\/a><\/p><\/div>