The field of medical image segmentation is moving towards developing more efficient and accurate models, particularly in resource-constrained environments. Recent developments focus on designing lightweight networks that can capture local and global context efficiently, while also improving the robustness and generalization of models. Noteworthy papers include LFA-Net, which proposes a novel attention module for retinal vessel segmentation, and VeloxSeg, which achieves a 26% Dice improvement alongside increasing GPU throughput by 11x and CPU by 48x. Other notable works, such as U-MAN and MSD-KMamba, enhance existing architectures with multi-scale feature extraction and bidirectional spatial perception, resulting in state-of-the-art performance on various benchmarks. Additionally, papers like PVTAdpNet and BALR-SAM demonstrate the effectiveness of integrating vision transformers and low-rank adaptation frameworks for accurate and efficient segmentation. Overall, the field is advancing towards more efficient, accurate, and robust medical image segmentation models.
Efficient Medical Image Segmentation
Sources
LFA-Net: A Lightweight Network with LiteFusion Attention for Retinal Vessel Segmentation
Johnson-Lindenstrauss Lemma Guided Network for Efficient 3D Medical Segmentation
U-MAN: U-Net with Multi-scale Adaptive KAN Network for Medical Image Segmentation
MSD-KMamba: Bidirectional Spatial-Aware Multi-Modal 3D Brain Segmentation via Multi-scale Self-Distilled Fusion Strategy
PVTAdpNet: Polyp Segmentation using Pyramid vision transformer with a novel Adapter block
BALR-SAM: Boundary-Aware Low-Rank Adaptation of SAM for Resource-Efficient Medical Image Segmentation
An Enhanced Pyramid Feature Network Based on Long-Range Dependencies for Multi-Organ Medical Image Segmentation
Hybrid Approach for Enhancing Lesion Segmentation in Fundus Images