Lightweight AI for Real-Time Spinal Endoscopic Instance Segmentation
Published:Dec 26, 2025 11:07
•1 min read
•ArXiv
Analysis
This paper addresses the critical need for real-time instance segmentation in spinal endoscopy to aid surgeons. The challenge lies in the demanding surgical environment (narrow field of view, artifacts, etc.) and the constraints of surgical hardware. The proposed LMSF-A framework offers a lightweight and efficient solution, balancing accuracy and speed, and is designed to be stable even with small batch sizes. The release of a new, clinically-reviewed dataset (PELD) is a valuable contribution to the field.
Key Takeaways
- •Proposes LMSF-A, a lightweight and efficient instance segmentation framework for spinal endoscopy.
- •Addresses challenges of real-time segmentation in a difficult surgical environment.
- •Employs novel architectural components like C2f-Pro, SSFF, TFE, and LMSH.
- •Introduces a new, clinically-reviewed PELD dataset.
- •Achieves competitive performance with significantly reduced computational cost.
Reference
“LMSF-A is highly competitive (or even better than) in all evaluation metrics and much lighter than most instance segmentation methods requiring only 1.8M parameters and 8.8 GFLOPs.”