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 751–775 of 2089 papers

Title	Date	Tasks	Status	Score
PGP-SAM: Prototype-Guided Prompt Learning for Efficient Few-Shot Medical Image Segmentation	Jan 12, 2025	Image SegmentationMedical Image Segmentation	CodeCode Available	5
Cross-dimensional transfer learning in medical image segmentation with deep learning	Jul 29, 2023	image-classificationImage Classification	CodeCode Available	5
PCDAL: A Perturbation Consistency-Driven Active Learning Approach for Medical Image Segmentation and Classification	Jun 29, 2023	Active LearningClassification	CodeCode Available	5
A Unified Loss for Handling Inter-Class and Intra-Class Imbalance in Medical Image Segmentation	Apr 11, 2025	Image SegmentationMedical Image Analysis	CodeCode Available	5
PCLMix: Weakly Supervised Medical Image Segmentation via Pixel-Level Contrastive Learning and Dynamic Mix Augmentation	May 10, 2024	Contrastive LearningDecoder	CodeCode Available	5
3D-EffiViTCaps: 3D Efficient Vision Transformer with Capsule for Medical Image Segmentation	Mar 25, 2024	DecoderHippocampus	CodeCode Available	5
Parameter Decoupling Strategy for Semi-supervised 3D Left Atrium Segmentation	Sep 20, 2021	Image SegmentationLeft Atrium Segmentation	CodeCode Available	5
AttResDU-Net: Medical Image Segmentation Using Attention-based Residual Double U-Net	Jun 25, 2023	Image SegmentationMedical Image Segmentation	CodeCode Available	5
Cost-Effective Active Learning for Melanoma Segmentation	Nov 24, 2017	Active LearningImage Segmentation	CodeCode Available	5
ACM-UNet: Adaptive Integration of CNNs and Mamba for Efficient Medical Image Segmentation	May 30, 2025	DecoderImage Segmentation	CodeCode Available	5
Overcoming Dimensional Collapse in Self-supervised Contrastive Learning for Medical Image Segmentation	Feb 22, 2024	Contrastive LearningImage Segmentation	CodeCode Available	5
Opinions Vary? Diagnosis First!	Feb 14, 2022	Medical Image SegmentationSegmentation	CodeCode Available	5
Optimizing Medical Image Segmentation with Advanced Decoder Design	Oct 5, 2024	Brain Tumor SegmentationDecoder	CodeCode Available	5
Optimizing the Dice Score and Jaccard Index for Medical Image Segmentation: Theory & Practice	Nov 5, 2019	Image SegmentationMedical Image Segmentation	CodeCode Available	5
ConvUNeXt: An efficient convolution neural network for medical image segmentation	Jul 25, 2022	DecoderImage Segmentation	CodeCode Available	5
ConvTransSeg: A Multi-resolution Convolution-Transformer Network for Medical Image Segmentation	Oct 13, 2022	DecoderImage Segmentation	CodeCode Available	5
On the Compactness, Efficiency, and Representation of 3D Convolutional Networks: Brain Parcellation as a Pretext Task	Jul 6, 2017	3D Medical Imaging SegmentationImage Segmentation	CodeCode Available	5
On the relationship between calibrated predictors and unbiased volume estimation	Dec 23, 2021	Active LearningImage Segmentation	CodeCode Available	5
One-pass Multi-task Networks with Cross-task Guided Attention for Brain Tumor Segmentation	Jun 5, 2019	Brain Tumor SegmentationImage Segmentation	CodeCode Available	5
On Enhancing Brain Tumor Segmentation Across Diverse Populations with Convolutional Neural Networks	May 5, 2024	Brain Tumor SegmentationImage Segmentation	CodeCode Available	5
One-shot Joint Extraction, Registration and Segmentation of Neuroimaging Data	Jul 27, 2023	Brain Image SegmentationImage Registration	CodeCode Available	5
Conv-MCD: A Plug-and-Play Multi-task Module for Medical Image Segmentation	Aug 14, 2019	Image SegmentationMedical Image Segmentation	CodeCode Available	5
On Image Segmentation With Noisy Labels: Characterization and Volume Properties of the Optimal Solutions to Accuracy and Dice	Jun 13, 2022	Image SegmentationMedical Image Segmentation	CodeCode Available	5
Contrastive Learning with Temporal Correlated Medical Images: A Case Study using Lung Segmentation in Chest X-Rays	Sep 7, 2021	Contrastive LearningImage Segmentation	CodeCode Available	5
ACNN: a Full Resolution DCNN for Medical Image Segmentation	Jan 26, 2019	Computed Tomography (CT)Image Segmentation	CodeCode Available	5

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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