Medical Image Segmentation On Synapse Multi

评估指标

Avg DSC

Avg HD

评测结果

各个模型在此基准测试上的表现结果

			Paper Title
Interactive AI-SAM gt box	90.66	-	AI-SAM: Automatic and Interactive Segment Anything Model
Medical SAM Adapter	89.80	-	Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation
MedSegDiff-v2	89.50	-	MedSegDiff-V2: Diffusion based Medical Image Segmentation with Transformer
nnUNet	88.80	10.78	nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation
MedNeXt-L (5x5x5)	88.76	-	MedNeXt: Transformer-driven Scaling of ConvNets for Medical Image Segmentation
MIST	86.92	11.07	MIST: Medical Image Segmentation Transformer with Convolutional Attention Mixing (CAM) Decoder
nnFormer	86.57	10.63	nnFormer: Interleaved Transformer for Volumetric Segmentation
AgileFormer	86.11	12.88	AgileFormer: Spatially Agile Transformer UNet for Medical Image Segmentation
MERIT	84.90	13.22	Multi-scale Hierarchical Vision Transformer with Cascaded Attention Decoding for Medical Image Segmentation
Automatic AI-SAM	84.21	-	AI-SAM: Automatic and Interactive Segment Anything Model
ParaTransCNN	83.86	15.86	ParaTransCNN: Parallelized TransCNN Encoder for Medical Image Segmentation
EMCAD	83.63	15.68	EMCAD: Efficient Multi-scale Convolutional Attention Decoding for Medical Image Segmentation
PAG-TransYnet	83.43	15.82	Rethinking Attention Gated with Hybrid Dual Pyramid Transformer-CNN for Generalized Segmentation in Medical Imaging
SegFormer3D	82.15	-	SegFormer3D: an Efficient Transformer for 3D Medical Image Segmentation
MISSFormer	81.96	18.20	MISSFormer: An Effective Medical Image Segmentation Transformer
TransUNet	81.19	-	S2S2: Semantic Stacking for Robust Semantic Segmentation in Medical Imaging
SelfReg-UNet: SwinUNet	80.54	-	SelfReg-UNet: Self-Regularized UNet for Medical Image Segmentation
SelfReg-UNet: Vanilla UNet	80.34	-	SelfReg-UNet: Self-Regularized UNet for Medical Image Segmentation
FCB Former	80.26	-	Adaptive t-vMF Dice Loss for Multi-class Medical Image Segmentation
SETR	79.60	-	Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers

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