{"id":2445,"date":"2024-05-03T19:23:25","date_gmt":"2024-05-03T19:23:25","guid":{"rendered":"https:\/\/labs.cs.queensu.ca\/perklab\/members\/jessica-rodgers\/"},"modified":"2024-05-03T19:23:25","modified_gmt":"2024-05-03T19:23:25","slug":"jessica-rodgers","status":"publish","type":"qsc_member","link":"https:\/\/labs.cs.queensu.ca\/perklab\/members\/jessica-rodgers\/","title":{"rendered":"Jessica Rodgers"},"content":{"rendered":"
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Jessica Rodgers<\/h1>\n<\/div>\n
Postdoctoral Fellow<\/div>\n
School of Computing<\/div>\n
Queen’s University<\/div>\n
Member from 2021<\/em> to present<\/em><\/div>\n
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\n\tJessica Rodgers is a postdoctoral fellow in the School of Computing, Queen’s University. She completed her undergraduate degree in Mathematics and Engineering at Queen’s University in 2014 and her PhD in Biomedical Engineering (Imaging) at Western University in 2020. Her PhD research focused on creating intraoperative ultrasound imaging tools to improve brachytherapy treatment of gynaecological cancers.
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March, Lucas; Rodgers, Jessica R.; Hisey, Rebecca; Jamzad, Amoon; Santilli, AML; McKay, D; Rudan, JF; Kaufmann, M; Ren, KYM; Fichtinger, G; Mousavi, P<\/p>

Cautery tool state detection using deep learning on intraoperative surgery videos<\/a> Journal Article<\/span> <\/p>

In: <\/span>vol. 12466, <\/span>pp. 89-95, <\/span>2023<\/span>.<\/p>

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

@article{fichtinger2023o,
\r\ntitle = {Cautery tool state detection using deep learning on intraoperative surgery videos},
\r\nauthor = {Lucas March and Jessica R. Rodgers and Rebecca Hisey and Amoon Jamzad and AML Santilli and D McKay and JF Rudan and M Kaufmann and KYM Ren and G Fichtinger and P Mousavi},
\r\nurl = {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},
\r\nyear  = {2023},
\r\ndate = {2023-01-01},
\r\nurldate = {2023-01-01},
\r\nvolume = {12466},
\r\npages = {89-95},
\r\npublisher = {SPIE},
\r\nabstract = {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 \u2026},
\r\nkeywords = {},
\r\npubstate = {published},
\r\ntppubtype = {article}
\r\n}
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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 \u2026<\/div>
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