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

Showing 1251–1300 of 2089 papers

TitleStatusHype
Medical Image Segmentation via Sparse Coding Decoder—0
Medical Image Segmentation with Belief Function Theory and Deep Learning—0
Medical image segmentation with imperfect 3D bounding boxes—0
Medical Image Segmentation with Limited Supervision: A Review of Deep Network Models—0
Medical Image Segmentation with SAM-generated Annotations—0
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
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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
4RaBiTmean Dice0.95—Unverified
5Yolo-SAM 2mean 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