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 1551–1600 of 2089 papers

TitleStatusHype
Indirect Supervision to Mitigate Perturbations—0
Informing selection of performance metrics for medical image segmentation evaluation using configurable synthetic errors—0
Learning Confident Classifiers in the Presence of Label Noise—0
Integrating Mamba Sequence Model and Hierarchical Upsampling Network for Accurate Semantic Segmentation of Multiple Sclerosis Legion—0
Interactive Deep Refinement Network for Medical Image Segmentation—0
Interactive Image Selection and Training for Brain Tumor Segmentation Network—0
Interactive Medical Image Segmentation using Deep Learning with Image-specific Fine-tuning—0
Interactive Medical Image Segmentation with Self-Adaptive Confidence Calibration—0
Interactive Segmentation via Deep Learning and B-Spline Explicit Active Surfaces—0
Interpretable and synergistic deep learning for visual explanation and statistical estimations of segmentation of disease features from medical images—0
Inter-Scale Dependency Modeling for Skin Lesion Segmentation with Transformer-based Networks—0
Inter-slice image augmentation based on frame interpolation for boosting medical image segmentation accuracy—0
Introducing A Novel Method For Adaptive Thresholding In Brain Tumor Medical Image Segmentation—0
Introducing Shape Prior Module in Diffusion Model for Medical Image Segmentation—0
Invariant Content Synergistic Learning for Domain Generalization of Medical Image Segmentation—0
Invertible Residual Network with Regularization for Effective Medical Image Segmentation—0
Investigating and Improving Latent Density Segmentation Models for Aleatoric Uncertainty Quantification in Medical Imaging—0
Investigation of Energy-efficient AI Model Architectures and Compression Techniques for "Green" Fetal Brain Segmentation—0
Is Foreground Prototype Sufficient? Few-Shot Medical Image Segmentation with Background-Fused Prototype—0
Is Long Range Sequential Modeling Necessary For Colorectal Tumor Segmentation?—0
Is SAM 2 Better than SAM in Medical Image Segmentation?—0
Iterative Deep Convolutional Encoder-Decoder Network for Medical Image Segmentation—0
Iteratively-Refined Interactive 3D Medical Image Segmentation with Multi-Agent Reinforcement Learning—0
J-CaPA : Joint Channel and Pyramid Attention Improves Medical Image Segmentation—0
Joint Liver and Hepatic Lesion Segmentation in MRI using a Hybrid CNN with Transformer Layers—0
Joint Modeling of Image and Label Statistics for Enhancing Model Generalizability of Medical Image Segmentation—0
Joint shape learning and segmentation for medical images using a minimalistic deep network—0
Just Say Better or Worse: A Human-AI Collaborative Framework for Medical Image Segmentation Without Manual Annotations—0
KA^2ER: Knowledge Adaptive Amalgamation of ExpeRts for Medical Images Segmentation—0
KANDU-Net:A Dual-Channel U-Net with KAN for Medical Image Segmentation—0
KAN-Mamba FusionNet: Redefining Medical Image Segmentation with Non-Linear Modeling—0
Kernel-U-Net: Multivariate Time Series Forecasting using Custom Kernels—0
Kidney and Kidney Tumor Segmentation using a Logical Ensemble of U-nets with Volumetric Validation—0
KiPA22 Report: U-Net with Contour Regularization for Renal Structures Segmentation—0
Knowledge-based Fully Convolutional Network and Its Application in Segmentation of Lung CT Images—0
Knowledge distillation from multi-modal to mono-modal segmentation networks—0
Label-efficient Hybrid-supervised Learning for Medical Image Segmentation—0
Label Filling via Mixed Supervision for Medical Image Segmentation from Noisy Annotations—0
Label noise in segmentation networks : mitigation must deal with bias—0
Label Sharing Incremental Learning Framework for Independent Multi-Label Segmentation Tasks—0
Language-guided Medical Image Segmentation with Target-informed Multi-level Contrastive Alignments—0
Large Batch and Patch Size Training for Medical Image Segmentation—0
Large-Kernel Attention for 3D Medical Image Segmentation—0
Layer Ensembles: A Single-Pass Uncertainty Estimation in Deep Learning for Segmentation—0
LDMRes-Net: Enabling Efficient Medical Image Segmentation on IoT and Edge Platforms—0
Learning Active Contour Models for Medical Image Segmentation—0
Learning Robust Medical Image Segmentation from Multi-source Annotations—0
Learning a sparse database for patch-based medical image segmentation—0
Learning Better Registration to Learn Better Few-Shot Medical Image Segmentation: Authenticity, Diversity, and Robustness—0
Learning Normalized Inputs for Iterative Estimation in 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