Phase-Informed Tool Segmentation for Manual Small-Incision Cataract Surgery

• URL: http://arxiv.org/abs/2411.16794v1
• Date: Mon, 25 Nov 2024 09:22:42 GMT
• Title: Phase-Informed Tool Segmentation for Manual Small-Incision Cataract Surgery
• Authors: Bhuvan Sachdeva, Naren Akash, Tajamul Ashraf, Simon Muller, Thomas Schultz, Maximilian W. M. Wintergerst, Niharika Singri Prasad, Kaushik Murali, Mohit Jain,
• Abstract summary: Cataract surgery is the most common surgical procedure globally, with a disproportionately higher burden in developing countries. We introduce Cataract-MSICS, the first comprehensive dataset containing 53 surgical videos annotated for 18 surgical phases and 3,527 frames with 13 surgical tools at the pixel level. We present ToolSeg, a novel framework that enhances tool segmentation by introducing a phase-conditional decoder and a simple yet effective semi-supervised setup leveraging pseudo-labels from foundation models.
• Score: 5.346116837157231
• License:
• Abstract: Cataract surgery is the most common surgical procedure globally, with a disproportionately higher burden in developing countries. While automated surgical video analysis has been explored in general surgery, its application to ophthalmic procedures remains limited. Existing works primarily focus on Phaco cataract surgery, an expensive technique not accessible in regions where cataract treatment is most needed. In contrast, Manual Small-Incision Cataract Surgery (MSICS) is the preferred low-cost, faster alternative in high-volume settings and for challenging cases. However, no dataset exists for MSICS. To address this gap, we introduce Cataract-MSICS, the first comprehensive dataset containing 53 surgical videos annotated for 18 surgical phases and 3,527 frames with 13 surgical tools at the pixel level. We benchmark this dataset on state-of-the-art models and present ToolSeg, a novel framework that enhances tool segmentation by introducing a phase-conditional decoder and a simple yet effective semi-supervised setup leveraging pseudo-labels from foundation models. Our approach significantly improves segmentation performance, achieving a $23.77\%$ to $38.10\%$ increase in mean Dice scores, with a notable boost for tools that are less prevalent and small. Furthermore, we demonstrate that ToolSeg generalizes to other surgical settings, showcasing its effectiveness on the CaDIS dataset.

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