Surgical SAM 2: Real-time Segment Anything in Surgical Video by Efficient Frame Pruning
This work addresses real-time surgical video segmentation for computer-assisted surgery, making it feasible in resource-constrained environments, though it is incremental as it builds on SAM2.
The paper tackled the inefficiency of Segment Anything Model 2 (SAM2) in processing high-resolution surgical videos by introducing Surgical SAM 2 (SurgSAM2) with an Efficient Frame Pruning mechanism, achieving a 3× FPS improvement and state-of-the-art segmentation accuracy after fine-tuning.
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.