MedBiSeNet: Efficient Bilateral Segmentation Network for Real-time Medical Image Processing

Medical image segmentation is a crucial component in modern computer-aided diagnosis, especially in colonoscopy which has a high failure rate (22-24%) in detecting polyps. Additionally, real-time processing on resource-limited devices is increasingly needed for immediate decision-making and supporting new approaches like capsule endoscopy. However, previous models suffered from either high computational costs or low accuracy due to the difficulty of handling ambiguous boundaries of medical images. This paper presents MedBiSeNet, a high-performance network for real-time medical image segmentation. The MedBiSeNet addresses the challenges with the following features: 1) Boundary-enhanced Bilateral Path to extract accurate boundary information, 2) Noise Refining Feature Fusion Module (NR-FFM) to eliminate unnecessary information and fuse features from bilateral branches. As a result, MedBiSeNet achieves state-of-the-art performance on polyp segmentation datasets (Kvasir-SEG, CVC-ClinicDB, ETIS-LARIBPOLYDB) with dice scores exceeding 0.96 while reducing parameters by 81.9% and computations by 95.4% compared to previous models, achieving 20.7 FPS on NVIDIA's Jetson TX2.