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 201–225 of 2089 papers

Title	Date	Tasks	Status	Hype
Discrepancy Matters: Learning from Inconsistent Decoder Features for Consistent Semi-supervised Medical Image Segmentation	Sep 26, 2023	DecoderImage Segmentation	CodeCode Available	1
Disentangling Human Error from Ground Truth in Segmentation of Medical Images	Dec 1, 2020	Medical Image SegmentationSegmentation	CodeCode Available	1
DC-UNet: Rethinking the U-Net Architecture with Dual Channel Efficient CNN for Medical Images Segmentation	May 31, 2020	DecoderImage Segmentation	CodeCode Available	1
Beyond pixel-wise supervision for segmentation: A few global shape descriptors might be surprisingly good!	May 3, 2021	Image SegmentationMedical Image Segmentation	CodeCode Available	1
D-Net: Dynamic Large Kernel with Dynamic Feature Fusion for Volumetric Medical Image Segmentation	Mar 15, 2024	Brain Tumor SegmentationImage Segmentation	CodeCode Available	1
DoDNet: Learning to segment multi-organ and tumors from multiple partially labeled datasets	Nov 20, 2020	Image SegmentationMedical Image Segmentation	CodeCode Available	1
A SAM-guided and Match-based Semi-Supervised Segmentation Framework for Medical Imaging	Nov 25, 2024	Image SegmentationMedical Image Segmentation	CodeCode Available	1
A Teacher-Student Framework for Semi-supervised Medical Image Segmentation From Mixed Supervision	Oct 23, 2020	DecoderImage Segmentation	CodeCode Available	1
Bi-Directional ConvLSTM U-Net with Densley Connected Convolutions	Aug 31, 2019	Image SegmentationLesion Segmentation	CodeCode Available	1
DoubleU-Net: A Deep Convolutional Neural Network for Medical Image Segmentation	Jun 8, 2020	Cell SegmentationColorectal Polyps Characterization	CodeCode Available	1
DS-TransUNet:Dual Swin Transformer U-Net for Medical Image Segmentation	Jun 12, 2021	DecoderImage Segmentation	CodeCode Available	1
Dual Cross-Attention for Medical Image Segmentation	Mar 30, 2023	Decoder	CodeCode Available	1
DDANet: Dual Decoder Attention Network for Automatic Polyp Segmentation	Dec 30, 2020	Colorectal Polyps CharacterizationDecision Making	CodeCode Available	1
Dual-Task Mutual Learning for Semi-Supervised Medical Image Segmentation	Mar 8, 2021	Image SegmentationMedical Image Segmentation	CodeCode Available	1
Bayesian Pseudo Labels: Expectation Maximization for Robust and Efficient Semi-Supervised Segmentation	Aug 8, 2022	Image SegmentationMedical Image Segmentation	CodeCode Available	1
Ariadne's Thread:Using Text Prompts to Improve Segmentation of Infected Areas from Chest X-ray images	Jul 8, 2023	Image SegmentationMedical Image Segmentation	CodeCode Available	1
E2ENet: Dynamic Sparse Feature Fusion for Accurate and Efficient 3D Medical Image Segmentation	Dec 7, 2023	Brain Tumor SegmentationImage Segmentation	CodeCode Available	1
Attention Swin U-Net: Cross-Contextual Attention Mechanism for Skin Lesion Segmentation	Oct 30, 2022	Image SegmentationLesion Segmentation	CodeCode Available	1
DCSAU-Net: A Deeper and More Compact Split-Attention U-Net for Medical Image Segmentation	Feb 2, 2022	DecoderImage Segmentation	CodeCode Available	1
Attentive Symmetric Autoencoder for Brain MRI Segmentation	Sep 19, 2022	Image SegmentationMedical Image Segmentation	CodeCode Available	1
Deep Anatomical Federated Network (Dafne): An open client-server framework for the continuous, collaborative improvement of deep learning-based medical image segmentation	Feb 13, 2023	DiagnosticImage Segmentation	CodeCode Available	1
EffiDec3D: An Optimized Decoder for High-Performance and Efficient 3D Medical Image Segmentation	Jan 1, 2025	DecoderImage Segmentation	CodeCode Available	1
Latent Diffusion for Medical Image Segmentation: End to end learning for fast sampling and accuracy	Jul 17, 2024	DenoisingImage Denoising	CodeCode Available	1
Bi-level Learning of Task-Specific Decoders for Joint Registration and One-Shot Medical Image Segmentation	Jan 1, 2024	Data AugmentationImage Segmentation	CodeCode Available	1
CycleMix: A Holistic Strategy for Medical Image Segmentation from Scribble Supervision	Mar 3, 2022	Image SegmentationMedical Image Segmentation	CodeCode Available	1

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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	DuAT	mean Dice	0.95	—	Unverified
10	SegMed	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	nnFormer	Avg DSC	92.06	—	Unverified
10	Automatic AI-SAM	Avg DSC	92.06	—	Unverified

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