Surgical SAM 2: Real-time Segment Anything in Surgical Video by Efficient Frame Pruning

TL;DR

SurgSAM2 is a real-time surgical video segmentation model that employs efficient frame pruning to significantly reduce computational costs while maintaining high accuracy, enabling practical use in resource-limited surgical environments.

Contribution

The paper introduces SurgSAM2, a novel model combining SAM2 with an Efficient Frame Pruning mechanism for faster and more efficient surgical video segmentation.

Findings

Achieves 3x faster FPS compared to SAM2.

Maintains high segmentation accuracy with lower-resolution data.

Demonstrates significant efficiency improvements in surgical video analysis.

Abstract

Surgical video segmentation is a critical task in computer-assisted surgery and is vital for enhancing surgical quality and patient outcomes. Recently, the Segment Anything Model 2 (SAM2) framework has shown superior advancements in image and video segmentation. However, SAM2 struggles with efficiency due to the high computational demands of processing high-resolution images and complex and long-range temporal dynamics in surgical videos. To address these challenges, we introduce Surgical SAM 2 (SurgSAM2), an advanced model to utilize SAM2 with an Efficient Frame Pruning (EFP) mechanism, to facilitate real-time surgical video segmentation. The EFP mechanism dynamically manages the memory bank by selectively retaining only the most informative frames, reducing memory usage and computational cost while maintaining high segmentation accuracy. Our extensive experiments demonstrate that SurgSAM2 significantly improves both efficiency and segmentation accuracy compared to the vanilla SAM2. Remarkably, SurgSAM2 achieves a 3$\times$ FPS compared with SAM2, while also delivering state-of-the-art performance after fine-tuning with lower-resolution data. These advancements establish SurgSAM2 as a leading model for surgical video analysis, making real-time surgical video segmentation in resource-constrained environments a reality. Our source code is available at https://github.com/jinlab-imvr/Surgical-SAM-2.