Abstract
Surgeons must visually distinguish soft-tissues, such as nerves, from surrounding anatomy to prevent complications and optimize patient outcomes. An accurate nerve segmentation and analysis tool could provide useful insight for surgical decision-making. Here, we present an end-to-end, automatic deep learning computer vision algorithm to segment and measure nerves. Unlike traditional medical imaging, our unconstrained setup with accessible handheld digital cameras, along with the unstructured open surgery scene, makes this task uniquely challenging. We investigate one common procedure, thyroidectomy, during which surgeons must avoid damaging the recurrent laryngeal nerve (RLN), which is responsible for human speech. We evaluate our segmentation algorithm on a diverse dataset across varied and challenging settings of operating room image capture, and show strong segmentation performance in the optimal image capture condition. This work lays the foundation for future research in real-time tissue discrimination and integration of accessible, intelligent tools into open surgery to provide actionable insights.
Highlights
segmentation[33,34,35], as well as anatomical identification in a variety of procedures[36,37] such as laparoscopic cholecystectomy[38,39,40]
Due to the greater variety of open surgery scenes in contrast to the more controlled environments of endoscopy, laparoscopy, and robot-assisted surgery, it is critical to understand how to manage this variation in open surgery to best enable intraoperative anatomical analysis across the diverse and challenging settings of operating room image capture
Our results show that improved image capture during surgery may help to further refine this computer vision and Artificial intelligence (AI) method to assist the surgeon
Summary
segmentation[33,34,35], as well as anatomical identification in a variety of procedures[36,37] such as laparoscopic cholecystectomy[38,39,40]. Due to the greater variety of open surgery scenes in contrast to the more controlled environments of endoscopy, laparoscopy, and robot-assisted surgery, it is critical to understand how to manage this variation in open surgery to best enable intraoperative anatomical analysis across the diverse and challenging settings of operating room image capture. To this end, we analyze how image capture conditions impact the algorithm’s performance, with the hope that these insights will guide future work involving image capture for open surgery. This work lays the foundation for future translational study bringing AI to vision and discrimination for open surgery
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