{"id":2098,"date":"2024-08-25T19:04:04","date_gmt":"2024-08-25T19:04:04","guid":{"rendered":"https:\/\/labs.cs.queensu.ca\/perklab\/?post_type=qsc_member&p=2098"},"modified":"2024-12-05T15:53:11","modified_gmt":"2024-12-05T15:53:11","slug":"pavel-dumitru-cernelev","status":"publish","type":"qsc_member","link":"https:\/\/labs.cs.queensu.ca\/perklab\/members\/pavel-dumitru-cernelev\/","title":{"rendered":"Pavel-Dumitru Cernelev"},"content":{"rendered":"
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Pavel-Dumitru Cernelev<\/h1><\/div>\n\t\t
MSc Student<\/div>\n\t\t
School of Computing<\/div>\n\t\t
Queen’s University<\/div>\n\t\t
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18pdc1@queensu.ca<\/a><\/div>\n\t\t\t
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Publications<\/h2>\n\n\n
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Hashtrudi-Zaad, Kian; Ungi, Tamas; Yeung, Chris; Baum, Zachary; Cernelev, Pavel-Dumitru; Hage, Anthony N; Schlenger, Christopher; Fichtinger, Gabor<\/p>

Expert-guided optimization of ultrasound segmentation models for 3D spine imaging Journal Article<\/span> <\/p>

In: <\/span>pp. 680-685, <\/span>2024<\/span>.<\/p>

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

@article{hashtrudi-zaad2024,
\r\ntitle = {Expert-guided optimization of ultrasound segmentation models for 3D spine imaging},
\r\nauthor = {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},
\r\nyear  = {2024},
\r\ndate = {2024-01-01},
\r\npages = {680-685},
\r\npublisher = {IEEE},
\r\nabstract = {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 \u2026},
\r\nkeywords = {},
\r\npubstate = {published},
\r\ntppubtype = {article}
\r\n}
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Close<\/a><\/p><\/div>
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 \u2026<\/div>
Close<\/a><\/p><\/div><\/div><\/div>
 Cernelev, Pavel-Dumitru;  Moga, Kristof;  Groves, Leah;  Haidegger, Tam\u00e1s;  Fichtinger, Gabor;  Ungi, Tamas<\/p>
Determining boundaries of accurate tracking for electromagnetic sensors<\/a> Conference<\/span> <\/p>
SPIE, <\/span>2023<\/span>.<\/p>
Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>
@conference{Cernelev2023,
\r\ntitle = {Determining boundaries of accurate tracking for electromagnetic sensors},
\r\nauthor = {Pavel-Dumitru Cernelev and Kristof Moga and Leah Groves and Tam\u00e1s Haidegger and Gabor Fichtinger and Tamas Ungi},
\r\neditor = {Cristian A. Linte and Jeffrey H. Siewerdsen},
\r\ndoi = {10.1117\/12.2654428},
\r\nyear  = {2023},
\r\ndate = {2023-04-03},
\r\nurldate = {2023-04-03},
\r\npublisher = {SPIE},
\r\nkeywords = {},
\r\npubstate = {published},
\r\ntppubtype = {conference}
\r\n}
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Close<\/a><\/p><\/div>