See in Depth: Training-Free Surgical Scene Segmentation with Monocular Depth Priors
PositiveArtificial Intelligence
- A new framework called depth-guided surgical scene segmentation (DepSeg) has been proposed, which utilizes monocular depth as a geometric prior to enhance pixel-wise segmentation of laparoscopic scenes without the need for extensive training. This method leverages a pretrained monocular depth estimation network and the Segment Anything Model 2 (SAM2) to improve segmentation accuracy on the CholecSeg8k dataset, achieving a mean Intersection over Union (mIoU) of 35.9% compared to 14.7% from a direct baseline.
- The introduction of DepSeg is significant as it addresses the high cost and complexity associated with dense annotations in surgical scene segmentation, making it more accessible for computer-assisted surgery applications. By utilizing depth information, the framework not only reduces the need for extensive training data but also enhances the efficiency and effectiveness of surgical video analysis.
- This development reflects a broader trend in artificial intelligence where models are increasingly being designed to operate with minimal training data, particularly in specialized fields like surgery. The integration of depth-guided prompting and template matching in DepSeg aligns with ongoing efforts to improve segmentation techniques across various domains, including medical imaging and video analysis, highlighting the potential for AI to transform surgical practices.
— via World Pulse Now AI Editorial System
