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 1651–1700 of 2089 papers

TitleStatusHype
Medical Report Generation based on Segment-Enhanced Contrastive Representation Learning—0
Medical Semantic Segmentation with Diffusion Pretrain—0
Medical Visual Prompting (MVP): A Unified Framework for Versatile and High-Quality Medical Image Segmentation—0
MedMAP: Promoting Incomplete Multi-modal Brain Tumor Segmentation with Alignment—0
MedPrompt: LLM-CNN Fusion with Weight Routing for Medical Image Segmentation and Classification—0
MedSAGa: Few-shot Memory Efficient Medical Image Segmentation using Gradient Low-Rank Projection in SAM—0
MedSAM-CA: A CNN-Augmented ViT with Attention-Enhanced Multi-Scale Fusion for Medical Image Segmentation—0
MedSAM-U: Uncertainty-Guided Auto Multi-Prompt Adaptation for Reliable MedSAM—0
MedSegDiffNCA: Diffusion Models With Neural Cellular Automata for Skin Lesion Segmentation—0
MedSegNet10: A Publicly Accessible Network Repository for Split Federated Medical Image Segmentation—0
MedSeg-R: Medical Image Segmentation with Clinical Reasoning—0
MedSeg-R: Reasoning Segmentation in Medical Images with Multimodal Large Language Models—0
MedVisionLlama: Leveraging Pre-Trained Large Language Model Layers to Enhance Medical Image Segmentation—0
Memory Consistent Unsupervised Off-the-Shelf Model Adaptation for Source-Relaxed Medical Image Segmentation—0
Meta Corrupted Pixels Mining for Medical Image Segmentation—0
Meta-hallucinator: Towards Few-Shot Cross-Modality Cardiac Image Segmentation—0
A Novel Deep Learning Based Approach for Left Ventricle Segmentation in Echocardiography: MFP-Unet—0
MGFI-Net: A Multi-Grained Feature Integration Network for Enhanced Medical Image Segmentation—0
Mind The Gap: Alleviating Local Imbalance for Unsupervised Cross-Modality Medical Image Segmentation—0
Mitigating False Predictions In Unreasonable Body Regions—0
MixCL: Pixel label matters to contrastive learning—0
Mixed-Block Neural Architecture Search for Medical Image Segmentation—0
Mixed Prototype Consistency Learning for Semi-supervised Medical Image Segmentation—0
Mixed-Supervised Dual-Network for Medical Image Segmentation—0
Mixing Data Augmentation with Preserving Foreground Regions in Medical Image Segmentation—0
MixModule: Mixed CNN Kernel Module for Medical Image Segmentation—0
Mixture-of-Shape-Experts (MoSE): End-to-End Shape Dictionary Framework to Prompt SAM for Generalizable Medical Segmentation—0
MKIS-Net: A Light-Weight Multi-Kernel Network for Medical Image Segmentation—0
MLA-BIN: Model-level Attention and Batch-instance Style Normalization for Domain Generalization of Federated Learning on Medical Image Segmentation—0
MM-UNet: Meta Mamba UNet for Medical Image Segmentation—0
Modality-Agnostic Learning for Medical Image Segmentation Using Multi-modality Self-distillation—0
Modelling brain lesion volume in patches with CNN-based Poisson Regression—0
Modern Convex Optimization to Medical Image Analysis—0
Momentum Contrastive Voxel-wise Representation Learning for Semi-supervised Volumetric Medical Image Segmentation—0
More than Encoder: Introducing Transformer Decoder to Upsample—0
Morphological Operation Residual Blocks: Enhancing 3D Morphological Feature Representation in Convolutional Neural Networks for Semantic Segmentation of Medical Images—0
MOSformer: Momentum encoder-based inter-slice fusion transformer for medical image segmentation—0
MPS-AMS: Masked Patches Selection and Adaptive Masking Strategy Based Self-Supervised Medical Image Segmentation—0
MPSeg : Multi-Phase strategy for coronary artery Segmentation—0
MS-DCANet: A Novel Segmentation Network For Multi-Modality COVID-19 Medical Images—0
MSE-Nets: Multi-annotated Semi-supervised Ensemble Networks for Improving Segmentation of Medical Image with Ambiguous Boundaries—0
MSGDD-cGAN: Multi-Scale Gradients Dual Discriminator Conditional Generative Adversarial Network—0
MS-MT: Multi-Scale Mean Teacher with Contrastive Unpaired Translation for Cross-Modality Vestibular Schwannoma and Cochlea Segmentation—0
MS-NAS: Multi-Scale Neural Architecture Search for Medical Image Segmentation—0
MS-Twins: Multi-Scale Deep Self-Attention Networks for Medical Image Segmentation—0
MS-UNet-v2: Adaptive Denoising Method and Training Strategy for Medical Image Segmentation with Small Training Data—0
Multi-dimensional Fusion and Consistency for Semi-supervised Medical Image Segmentation—0
Multi-dimension Transformer with Attention-based Filtering for Medical Image Segmentation—0
Multi-encoder nnU-Net outperforms Transformer models with self-supervised pretraining—0
Multi-Granularity Vision Fastformer with Fusion Mechanism for Skin Lesion 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