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 1976–2000 of 2089 papers

Title	Date	Tasks	Status
GLoG-CSUnet: Enhancing Vision Transformers with Adaptable Radiomic Features for Medical Image Segmentation	Jan 6, 2025	Cardiac SegmentationImage Segmentation	CodeCode Available
GLIMS: Attention-Guided Lightweight Multi-Scale Hybrid Network for Volumetric Semantic Segmentation	Apr 27, 2024	BraTS2021Image Segmentation	CodeCode Available
Conditional Random Fields as Recurrent Neural Networks for 3D Medical Imaging Segmentation	Jul 19, 2018	3D Medical Imaging SegmentationMedical Image Segmentation	CodeCode Available
Generalizable Single-Source Cross-modality Medical Image Segmentation via Invariant Causal Mechanisms	Nov 7, 2024	Domain GeneralizationImage Segmentation	CodeCode Available
3D-OOCS: Learning Prostate Segmentation with Inductive Bias	Oct 29, 2021	Edge DetectionImage Segmentation	CodeCode Available
Gaussian Random Fields as an Abstract Representation of Patient Metadata for Multimodal Medical Image Segmentation	Mar 7, 2025	Image SegmentationMedical Image Segmentation	CodeCode Available
3D Medical Image Segmentation based on multi-scale MPU-Net	Jul 11, 2023	Image SegmentationMedical Image Segmentation	CodeCode Available
From Patch to Image Segmentation using Fully Convolutional Networks -- Application to Retinal Images	Apr 8, 2019	Image SegmentationMedical Image Segmentation	CodeCode Available
From Gaze to Insight: Bridging Human Visual Attention and Vision Language Model Explanation for Weakly-Supervised Medical Image Segmentation	Apr 15, 2025	DiagnosticImage Segmentation	CodeCode Available
Combo Loss: Handling Input and Output Imbalance in Multi-Organ Segmentation	May 8, 2018	Image SegmentationMedical Image Segmentation	CodeCode Available
Assessing Test-time Variability for Interactive 3D Medical Image Segmentation with Diverse Point Prompts	Nov 13, 2023	Image SegmentationInteractive Segmentation	CodeCode Available
Assessing Reliability and Challenges of Uncertainty Estimations for Medical Image Segmentation	Jul 7, 2019	Image SegmentationMedical Image Segmentation	CodeCode Available
FocusNet: An attention-based Fully Convolutional Network for Medical Image Segmentation	Feb 8, 2019	Image SegmentationLesion Segmentation	CodeCode Available
FMG-Net and W-Net: Multigrid Inspired Deep Learning Architectures For Medical Imaging Segmentation	Apr 5, 2023	Brain Tumor SegmentationImage Segmentation	CodeCode Available
FF-UNet: a U-Shaped Deep Convolutional Neural Network for Multimodal Biomedical Image Segmentation	Jun 27, 2022	Image SegmentationMedical Image Segmentation	CodeCode Available
Sauron U-Net: Simple automated redundancy elimination in medical image segmentation via filter pruning	Sep 27, 2022	Image SegmentationMedical Image Segmentation	CodeCode Available
Color-Quality Invariance for Robust Medical Image Segmentation	Feb 11, 2025	Domain GeneralizationImage Segmentation	CodeCode Available
3D fully convolutional networks for subcortical segmentation in MRI: A large-scale study	Dec 12, 2016	3D Medical Imaging SegmentationBrain Segmentation	CodeCode Available
Scale-aware Test-time Click Adaptation for Pulmonary Nodule and Mass Segmentation	Jul 28, 2023	Image SegmentationManagement	CodeCode Available
Scale-Equivariant Deep Learning for 3D Data	Apr 12, 2023	Deep LearningImage Segmentation	CodeCode Available
Few-shot 3D Multi-modal Medical Image Segmentation using Generative Adversarial Learning	Oct 29, 2018	3D Medical Imaging SegmentationBrain Image Segmentation	CodeCode Available
FESS Loss: Feature-Enhanced Spatial Segmentation Loss for Optimizing Medical Image Analysis	Feb 13, 2024	Contrastive LearningImage Segmentation	CodeCode Available
TK-Mamba: Marrying KAN with Mamba for Text-Driven 3D Medical Image Segmentation	May 24, 2025	Image SegmentationKolmogorov-Arnold Networks	CodeCode Available
Y-Net: Joint Segmentation and Classification for Diagnosis of Breast Biopsy Images	Jun 4, 2018	DescriptiveDiagnostic	CodeCode Available
Token Sparsification for Faster Medical Image Segmentation	Mar 11, 2023	Image SegmentationMedical Image Segmentation	CodeCode Available

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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