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

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Showing 1351–1375 of 2089 papers

Title	Date	Tasks	Status	Hype
ConFUDA: Contrastive Fewshot Unsupervised Domain Adaptation for Medical Image Segmentation	Jun 8, 2022	Contrastive LearningDomain Adaptation	—Unverified	0
Implementation of a Modified U-Net for Medical Image Segmentation on Edge Devices	Jun 6, 2022	Image SegmentationMedical Image Segmentation	—Unverified	0
Bootstrapping Semi-supervised Medical Image Segmentation with Anatomical-aware Contrastive Distillation	Jun 6, 2022	Contrastive LearningImage Segmentation	CodeCode Available	1
ACT: Semi-supervised Domain-adaptive Medical Image Segmentation with Asymmetric Co-training	Jun 5, 2022	Domain AdaptationImage Segmentation	—Unverified	0
Patcher: Patch Transformers with Mixture of Experts for Precise Medical Image Segmentation	Jun 3, 2022	DecoderImage Segmentation	CodeCode Available	1
R2U++: A Multiscale Recurrent Residual U-Net with Dense Skip Connections for Medical Image Segmentation	Jun 3, 2022	Computed Tomography (CT)Decoder	—Unverified	0
Mutual- and Self- Prototype Alignment for Semi-supervised Medical Image Segmentation	Jun 3, 2022	Image SegmentationMedical Image Segmentation	—Unverified	0
Incremental Learning Meets Transfer Learning: Application to Multi-site Prostate MRI Segmentation	Jun 3, 2022	Image SegmentationIncremental Learning	—Unverified	0
MaxStyle: Adversarial Style Composition for Robust Medical Image Segmentation	Jun 2, 2022	Data AugmentationDecoder	CodeCode Available	1
Adaptive Adversarial Training to Improve Adversarial Robustness of DNNs for Medical Image Segmentation and Detection	Jun 2, 2022	Adversarial RobustnessImage Segmentation	—Unverified	0
Transforming medical imaging with Transformers? A comparative review of key properties, current progresses, and future perspectives	Jun 2, 2022	Image SegmentationMedical Image Segmentation	—Unverified	0
MISSU: 3D Medical Image Segmentation via Self-distilling TransUNet	Jun 2, 2022	Image SegmentationMedical Image Segmentation	CodeCode Available	1
The Fully Convolutional Transformer for Medical Image Segmentation	Jun 1, 2022	Image SegmentationMedical Image Analysis	CodeCode Available	1
Dynamic Linear Transformer for 3D Biomedical Image Segmentation	Jun 1, 2022	3D Medical Imaging SegmentationDecoder	CodeCode Available	0
Memory-efficient Segmentation of High-resolution Volumetric MicroCT Images	May 31, 2022	GPUImage Segmentation	CodeCode Available	0
Segmentation Consistency Training: Out-of-Distribution Generalization for Medical Image Segmentation	May 30, 2022	Data AugmentationImage Segmentation	CodeCode Available	0
Decoupled Pyramid Correlation Network for Liver Tumor Segmentation from CT images	May 26, 2022	Computed Tomography (CT)Image Segmentation	—Unverified	0
Structure Unbiased Adversarial Model for Medical Image Segmentation	May 25, 2022	Image SegmentationImage-to-Image Translation	—Unverified	0
UNet#: A UNet-like Redesigning Skip Connections for Medical Image Segmentation	May 24, 2022	DecoderImage Segmentation	—Unverified	0
Mind The Gap: Alleviating Local Imbalance for Unsupervised Cross-Modality Medical Image Segmentation	May 24, 2022	Cardiac SegmentationDisentanglement	—Unverified	0
Heuristic Dropout: An Efficient Regularization Method for Medical Image Segmentation Models	May 23, 2022	Image SegmentationMedical Image Segmentation	CodeCode Available	0
Self-supervised 3D anatomy segmentation using self-distilled masked image transformer (SMIT)	May 20, 2022	AnatomyImage Segmentation	CodeCode Available	1
3DConvCaps: 3DUnet with Convolutional Capsule Encoder for Medical Image Segmentation	May 19, 2022	DecoderHippocampus	CodeCode Available	1
ColonFormer: An Efficient Transformer based Method for Colon Polyp Segmentation	May 17, 2022	DecoderMedical Image Segmentation	CodeCode Available	1
Voxel-wise Adversarial Semi-supervised Learning for Medical Image Segmentation	May 14, 2022	Image SegmentationMedical Image Segmentation	—Unverified	0

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All datasets Kvasir-SEG CVC-ClinicDB CVC-ColonDB ETIS-LARIBPOLYPDB Synapse multi-organ CT Automatic Cardiac Diagnosis Challenge (ACDC)MoNuSeg 2018 Data Science Bowl GlaS BKAI-IGH NeoPolyp-Small MICCAI 2015 Multi-Atlas Abdomen Labeling Challenge ACDC

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	DUCK-Net	mean Dice	0.95	—	Unverified
2	EffiSegNet-B5	mean Dice	0.95	—	Unverified
3	EffiSegNet-B4	mean Dice	0.95	—	Unverified
4	SegMed	mean Dice	0.95	—	Unverified
5	FCB Former	mean Dice	0.94	—	Unverified
6	FCB-SwinV2 Transformer	mean Dice	0.94	—	Unverified
7	SEP	mean Dice	0.94	—	Unverified
8	LM-Net	mean Dice	0.94	—	Unverified
9	RAPUNet	mean Dice	0.94	—	Unverified
10	FCBFormer	mean Dice	0.94	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	DUCK-Net	mean Dice	0.97	—	Unverified
2	RAPUNet	mean Dice	0.96	—	Unverified
3	EMCAD	mean Dice	0.95	—	Unverified
4	Yolo-SAM 2	mean Dice	0.95	—	Unverified
5	RaBiT	mean Dice	0.95	—	Unverified
6	UGCANet	mean Dice	0.95	—	Unverified
7	ESFPNet-L	mean Dice	0.95	—	Unverified
8	FCBFormer	mean Dice	0.95	—	Unverified
9	SegMed	mean Dice	0.95	—	Unverified
10	DuAT	mean Dice	0.95	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	RAPUNet	mean Dice	0.95	—	Unverified
2	DUCK-Net	mean Dice	0.94	—	Unverified
3	EMCAD	mean Dice	0.92	—	Unverified
4	SegMed	mean Dice	0.92	—	Unverified
5	UniNet	mean Dice	0.92	—	Unverified
6	ProMISe	mean Dice	0.87	—	Unverified
7	Meta-Polyp	mean Dice	0.87	—	Unverified
8	ResUNet++ + TTA	mean Dice	0.85	—	Unverified
9	PVT-GCASCADE	mean Dice	0.83	—	Unverified
10	PVT-CASCADE	mean Dice	0.83	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	RAPUNet	mean Dice	0.96	—	Unverified
2	SegMed	mean Dice	0.94	—	Unverified
3	DUCK-Net	mean Dice	0.94	—	Unverified
4	EMCAD	mean Dice	0.92	—	Unverified
5	ProMISe	mean Dice	0.84	—	Unverified
6	RSAFormer	mean Dice	0.84	—	Unverified
7	ESFPNet-L	mean Dice	0.82	—	Unverified
8	DuAT	mean Dice	0.82	—	Unverified
9	PVT-CASCADE	mean Dice	0.8	—	Unverified
10	SSFormer-L	mean Dice	0.8	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Interactive AI-SAM gt box	Avg DSC	90.66	—	Unverified
2	Medical SAM Adapter	Avg DSC	89.8	—	Unverified
3	MedSegDiff-v2	Avg DSC	89.5	—	Unverified
4	nnUNet	Avg DSC	88.8	—	Unverified
5	MedNeXt-L (5x5x5)	Avg DSC	88.76	—	Unverified
6	MIST	Avg DSC	86.92	—	Unverified
7	nnFormer	Avg DSC	86.57	—	Unverified
8	AgileFormer	Avg DSC	86.11	—	Unverified
9	MERIT	Avg DSC	84.9	—	Unverified
10	Automatic AI-SAM	Avg DSC	84.21	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	FCT	Avg DSC	94.26	—	Unverified
2	Interactive AI-SAM gt box	Avg DSC	93.89	—	Unverified
3	FCT	Avg DSC	93.02	—	Unverified
4	LHU-Net	Avg DSC	92.65	—	Unverified
5	MIST	Avg DSC	92.56	—	Unverified
6	MERIT	Avg DSC	92.32	—	Unverified
7	MERIT-GCASCADE	Avg DSC	92.23	—	Unverified
8	EMCAD	Avg DSC	92.12	—	Unverified
9	Automatic AI-SAM	Avg DSC	92.06	—	Unverified
10	nnFormer	Avg DSC	92.06	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Stardist	F1	84.6	—	Unverified