Mr. Lucas March Profile

Lucas March

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Proceedings Article | 3 April 2023 Presentation + Paper

Cautery tool state detection using deep learning on intraoperative surgery videos

L. March, J. Rodgers, R. Hisey, A. Jamzad, A. M. Santilli, D. McKay, J. Rudan, M. Kaufmann, K. Y. Ren, G. Fichtinger, P. Mousavi

Proceedings Volume 12466, 124660D (2023) https://doi.org/10.1117/12.2654234

KEYWORDS: Surgery, Video, Resection, Object detection, Education and training, Data modeling, Deep learning, Cameras, Skin, Tissues

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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, our burn prediction model leverages the effectiveness of both a Long Short-Term Memory (LSTM) network and a Receiver Operating Characteristic (ROC) curve to accurately predict when the surgeon is cutting. The cut identifications will be used in the future for synchronization with iKnife data to provide localizations when cuts are predicted. The model was trained with four-fold cross-validation on a patient-wise split between training, validation, and testing sets. Average recall over the four folds of testing for the LSTM and ROC were 0.80 and 0.73, respectively. The video-based approach is simple yet effective at identifying tool-to-skin contact instances and may help guide surgeons, enabling them to deliver precise treatments in combination with iKnife data.

Proceedings Article | 4 April 2022 Poster + Presentation + Paper

Phase recognition and cautery localization in basal cell carcinoma surgical videos

L. March, J. Rodgers, A. Jamzad, A. M. L. Santilli, R. Hisey, D. McKay, J. Rudan, M. Kaufmann, K.Y.M. Ren, G. Fichtinger, P. Mousavi

Proceedings Volume 12034, 1203427 (2022) https://doi.org/10.1117/12.2611837

KEYWORDS: Surgery, Video, Data modeling, Data acquisition, Cameras, Detection and tracking algorithms, Tissues, Cancer, Process modeling

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Surgical excision for basal cell carcinoma (BCC) is a common treatment to remove the affected areas of skin. Minimizing positive margins around excised tissue is essential for successful treatment. Residual cancer cells may result in repeat surgery; however, detecting remaining cancer can be challenging and time-consuming. Using chemical signal data acquired while tissue is excised with a cautery tool, the iKnife system can discriminate between healthy and cancerous tissue but lacks spatial information, making it difficult to navigate back to suspicious margins. Intraoperative videos of BCC excision allow cautery locations to be tracked, providing the sites of potential positive margins. We propose a deep learning approach using convolutional neural networks to recognize phases in the videos and subsequently track the cautery location, comparing two localization methods (supervised and semi-supervised). Phase recognition was used for preprocessing to classify frames as showing the surgery or the start/stop of iKnife data acquisition. Only frames designated as showing the surgery were used for cautery localization. Fourteen videos were recorded during BCC excisions with iKnife data collection. On unseen testing data (2 videos, 1,832 frames), the phase recognition model showed an overall accuracy of 86%. Tool localization performed with a mean average precision of 0.98 and 0.96 for supervised and semisupervised methods, respectively, at a 0.5 intersection over union threshold. Incorporating intraoperative phase data with tool tracking provides surgeons with spatial information about the cautery tool location around suspicious regions, potentially improving the surgeon's ability to navigate back to the area of concern.

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