Evaluation of Methods for Detection and Semantic Segmentation of the Anterior Capsulotomy in Cataract Surgery Video

• Published in: Clinical ophthalmology (Auckland, N.Z.) Vol. 18; pp. 647 - 657
• Main Authors: Zeng, Zixue, Giap, Binh Duong, Kahana, Ethan, Lustre, Jefferson, Mahmoud, Ossama, Mian, Shahzad I, Tannen, Bradford, Nallasamy, Nambi
• Format: Journal Article
• Language: English
• Published: New Zealand Dove Medical Press Limited 01.01.2024; Dove; Dove Medical Press
• Subjects: capsulotomy; Cataract; cataract surgery; deep learning; image classification.; Image processing; image segmentation; Methods; Original Research; Surgery; Michigan; deep learning; image classification; image segmentation; capsulotomy; cataract surgery
• ISSN: 1177-5467; 1177-5483; 1177-5483
• DOI: 10.2147/OPTH.S453073

The capsulorhexis is one of the most important and challenging maneuvers in cataract surgery. Automated analysis of the anterior capsulotomy could aid surgical training through the provision of objective feedback and guidance to trainees. To develop and evaluate a deep learning-based system for the automated identification and semantic segmentation of the anterior capsulotomy in cataract surgery video. In this study, we established a BigCat-Capsulotomy dataset comprising 1556 video frames extracted from 190 recorded cataract surgery videos for developing and validating the capsulotomy recognition system. The proposed system involves three primary stages: video preprocessing, capsulotomy video frame classification, and capsulotomy segmentation. To thoroughly evaluate its efficacy, we examined the performance of a total of eight deep learning-based classification models and eleven segmentation models, assessing both accuracy and time consumption. Furthermore, we delved into the factors influencing system performance by deploying it across various surgical phases. The ResNet-152 model employed in the classification step of the proposed capsulotomy recognition system attained strong performance with an overall Dice coefficient of 92.21%. Similarly, the UNet model with the DenseNet-169 backbone emerged as the most effective segmentation model among those investigated, achieving an overall Dice coefficient of 92.12%. Moreover, the time consumption of the system was low at 103.37 milliseconds per frame, facilitating its application in real-time scenarios. Phase-wise analysis indicated that the Phacoemulsification phase (nuclear disassembly) was the most challenging to segment (Dice coefficient of 86.02%). The experimental results showed that the proposed system is highly effective in intraoperative capsulotomy recognition during cataract surgery and demonstrates both high accuracy and real-time capabilities. This system holds significant potential for applications in surgical performance analysis, education, and intraoperative guidance systems.