SOTAVerified

Medical Image Segmentation

Medical Image Segmentation is a computer vision task that involves dividing an medical image into multiple segments, where each segment represents a different object or structure of interest in the image. The goal of medical image segmentation is to provide a precise and accurate representation of the objects of interest within the image, typically for the purpose of diagnosis, treatment planning, and quantitative analysis.

( Image credit: IVD-Net )

Papers

Showing 1–10 of 2089 papers

TitleStatusHype
DiffOSeg: Omni Medical Image Segmentation via Multi-Expert Collaboration Diffusion ModelCode0
Unified Medical Image Segmentation with State Space Modeling Snake—0
U-RWKV: Lightweight medical image segmentation with direction-adaptive RWKVCode1
HNOSeg-XS: Extremely Small Hartley Neural Operator for Efficient and Resolution-Robust 3D Image Segmentation—0
Just Say Better or Worse: A Human-AI Collaborative Framework for Medical Image Segmentation Without Manual Annotations—0
Causal-SAM-LLM: Large Language Models as Causal Reasoners for Robust Medical Segmentation—0
SAMed-2: Selective Memory Enhanced Medical Segment Anything ModelCode1
Autoadaptive Medical Segment Anything ModelCode0
MedSAM-CA: A CNN-Augmented ViT with Attention-Enhanced Multi-Scale Fusion for Medical Image Segmentation—0
MedPrompt: LLM-CNN Fusion with Weight Routing for Medical Image Segmentation and Classification—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1QTSegAverage Dice (5-folds)93.13—Unverified
2RaBiTAverage Dice0.94—Unverified
3EMCADAverage Dice0.93—Unverified
4TransResU-NetAverage Dice0.92—Unverified
5TGANetAverage Dice0.9—Unverified
6NeoUNetAverage Dice0.81—Unverified
7FocalUNetAverage Dice0.8—Unverified
8BlazeNeoAverage Dice0.79—Unverified
9ColonSegNetAverage Dice0.69—Unverified