SOTAVerified

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 1901–1950 of 2089 papers

TitleStatusHype
Semi-Supervised Medical Image Segmentation via Cross Teaching between CNN and Transformer—0
Semi-supervised Medical Image Segmentation via Geometry-aware Consistency Training—0
Semi-Supervised Medical Image Segmentation with Co-Distribution Alignment—0
Semi-Supervised Medical Image Segmentation via Knowledge Mining from Large Models—0
Semi-Supervised Multi-Organ Segmentation through Quality Assurance Supervision—0
Semi-Supervised Segmentation via Embedding Matching—0
SemSim: Revisiting Weak-to-Strong Consistency from a Semantic Similarity Perspective for Semi-supervised Medical Image Segmentation—0
SESV: Accurate Medical Image Segmentation byPredicting and Correcting Errors—0
SeUNet-Trans: A Simple yet Effective UNet-Transformer Model for Medical Image Segmentation—0
Semantic-guided Disentangled Representation for Unsupervised Cross-modality Medical Image Segmentation—0
Shape constrained CNN for segmentation guided prediction of myocardial shape and pose parameters in cardiac MRI—0
ShapeMamba-EM: Fine-Tuning Foundation Model with Local Shape Descriptors and Mamba Blocks for 3D EM Image Segmentation—0
Shape of my heart: Cardiac models through learned signed distance functions—0
Show and Segment: Universal Medical Image Segmentation via In-Context Learning—0
Siamese Content Loss Networks for Highly Imbalanced Medical Image Segmentation—0
SimCVD: Simple Contrastive Voxel-Wise Representation Distillation for Semi-Supervised Medical Image Segmentation—0
Single-domain Generalization in Medical Image Segmentation via Test-time Adaptation from Shape Dictionary—0
Size Aware Cross-shape Scribble Supervision for Medical Image Segmentation—0
SkinSAM: Empowering Skin Cancer Segmentation with Segment Anything Model—0
SkrGAN: Sketching-rendering Unconditional Generative Adversarial Networks for Medical Image Synthesis—0
Slice Imputation: Intermediate Slice Interpolation for Anisotropic 3D Medical Image Segmentation—0
SliceMamba with Neural Architecture Search for Medical Image Segmentation—0
SL: Stable Learning in Source-Free Domain Adaption for Medical Image Segmentation—0
SM2C: Boost the Semi-supervised Segmentation for Medical Image by using Meta Pseudo Labels and Mixed Images—0
SMC-UDA: Structure-Modal Constraint for Unsupervised Cross-Domain Renal Segmentation—0
Source-Free Unsupervised Domain Adaptation with Norm and Shape Constraints for Medical Image Segmentation—0
Source Identification: A Self-Supervision Task for Dense Prediction—0
SP ^3 : Superpixel-propagated pseudo-label learning for weakly semi-supervised medical image segmentation—0
SPA: Efficient User-Preference Alignment against Uncertainty in Medical Image Segmentation—0
Spatial Correspondence between Graph Neural Network-Segmented Images—0
Spatially Dependent U-Nets: Highly Accurate Architectures for Medical Imaging Segmentation—0
Spatially Exclusive Pasting: A General Data Augmentation for the Polyp Segmentation—0
Spatio-Temporal Convolutional LSTMs for Tumor Growth Prediction by Learning 4D Longitudinal Patient Data—0
SPCTNet: A Series-Parallel CNN and Transformer Network for 3D Medical Image Segmentation—0
Spectral U-Net: Enhancing Medical Image Segmentation via Spectral Decomposition—0
SQA-SAM: Segmentation Quality Assessment for Medical Images Utilizing the Segment Anything Model—0
SS-3DCapsNet: Self-supervised 3D Capsule Networks for Medical Segmentation on Less Labeled Data—0
SSASS: Semi-Supervised Approach for Stenosis Segmentation—0
SSEGEP: Small SEGment Emphasized Performance evaluation metric for medical image segmentation—0
STM-UNet: An Efficient U-shaped Architecture Based on Swin Transformer and Multi-scale MLP for Medical Image Segmentation—0
Structure Unbiased Adversarial Model for Medical Image Segmentation—0
Style Augmentation improves Medical Image Segmentation—0
Style Curriculum Learning for Robust Medical Image Segmentation—0
Supervision by Denoising for Medical Image Segmentation—0
SuperCL: Superpixel Guided Contrastive Learning for Medical Image Segmentation Pre-training—0
SuperPatchMatch: an Algorithm for Robust Correspondences using Superpixel Patches—0
Superpixel-Guided Label Softening for Medical Image Segmentation—0
Superpixel Pre-Segmentation of HER2 Slides for Efficient Annotation—0
Support-Query Prototype Fusion Network for Few-shot Medical Image Segmentation—0
Surf-CDM: Score-Based Surface Cold-Diffusion Model For Medical Image Segmentation—0
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All datasetsKvasir-SEGCVC-ClinicDBCVC-ColonDBETIS-LARIBPOLYPDBSynapse multi-organ CTAutomatic Cardiac Diagnosis Challenge (ACDC)MoNuSeg2018 Data Science BowlGlaSBKAI-IGH NeoPolyp-SmallMICCAI 2015 Multi-Atlas Abdomen Labeling ChallengeACDC

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1DUCK-Netmean Dice0.95—Unverified
2EffiSegNet-B5mean Dice0.95—Unverified
3EffiSegNet-B4mean Dice0.95—Unverified
4SegMedmean Dice0.95—Unverified
5FCB Formermean Dice0.94—Unverified
6FCB-SwinV2 Transformermean Dice0.94—Unverified
7SEPmean Dice0.94—Unverified
8LM-Netmean Dice0.94—Unverified
9RAPUNetmean Dice0.94—Unverified
10FCBFormermean Dice0.94—Unverified
#ModelMetricClaimedVerifiedStatus
1DUCK-Netmean Dice0.97—Unverified
2RAPUNetmean Dice0.96—Unverified
3EMCADmean Dice0.95—Unverified
4Yolo-SAM 2mean Dice0.95—Unverified
5RaBiTmean Dice0.95—Unverified
6UGCANetmean Dice0.95—Unverified
7ESFPNet-Lmean Dice0.95—Unverified
8FCBFormermean Dice0.95—Unverified
9DuATmean Dice0.95—Unverified
10SegMedmean Dice0.95—Unverified
#ModelMetricClaimedVerifiedStatus
1RAPUNetmean Dice0.95—Unverified
2DUCK-Netmean Dice0.94—Unverified
3EMCADmean Dice0.92—Unverified
4SegMedmean Dice0.92—Unverified
5UniNetmean Dice0.92—Unverified
6ProMISemean Dice0.87—Unverified
7Meta-Polypmean Dice0.87—Unverified
8ResUNet++ + TTAmean Dice0.85—Unverified
9PVT-GCASCADEmean Dice0.83—Unverified
10PVT-CASCADEmean Dice0.83—Unverified
#ModelMetricClaimedVerifiedStatus
1RAPUNetmean Dice0.96—Unverified
2SegMedmean Dice0.94—Unverified
3DUCK-Netmean Dice0.94—Unverified
4EMCADmean Dice0.92—Unverified
5ProMISemean Dice0.84—Unverified
6RSAFormermean Dice0.84—Unverified
7ESFPNet-Lmean Dice0.82—Unverified
8DuATmean Dice0.82—Unverified
9PVT-CASCADEmean Dice0.8—Unverified
10SSFormer-Lmean Dice0.8—Unverified
#ModelMetricClaimedVerifiedStatus
1Interactive AI-SAM gt boxAvg DSC90.66—Unverified
2Medical SAM AdapterAvg DSC89.8—Unverified
3MedSegDiff-v2Avg DSC89.5—Unverified
4nnUNetAvg DSC88.8—Unverified
5MedNeXt-L (5x5x5)Avg DSC88.76—Unverified
6MISTAvg DSC86.92—Unverified
7nnFormerAvg DSC86.57—Unverified
8AgileFormerAvg DSC86.11—Unverified
9MERITAvg DSC84.9—Unverified
10Automatic AI-SAMAvg DSC84.21—Unverified
#ModelMetricClaimedVerifiedStatus
1FCTAvg DSC94.26—Unverified
2Interactive AI-SAM gt boxAvg DSC93.89—Unverified
3FCTAvg DSC93.02—Unverified
4LHU-NetAvg DSC92.65—Unverified
5MISTAvg DSC92.56—Unverified
6MERITAvg DSC92.32—Unverified
7MERIT-GCASCADEAvg DSC92.23—Unverified
8EMCADAvg DSC92.12—Unverified
9nnFormerAvg DSC92.06—Unverified
10Automatic AI-SAMAvg DSC92.06—Unverified
#ModelMetricClaimedVerifiedStatus
1StardistF184.6—Unverified