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 1201–1250 of 2089 papers

TitleStatusHype
Atrous Residual Interconnected Encoder to Attention Decoder Framework for Vertebrae Segmentation via 3D Volumetric CT Images—0
ATSO: Asynchronous Teacher-Student Optimization for Semi-Supervised Medical Image Segmentation—0
AttenScribble: Attentive Similarity Learning for Scribble-Supervised Medical Image Segmentation—0
Attentional Triple-Encoder Network in Spatiospectral Domains for Medical Image Segmentation—0
Mind the Context: Attention-Guided Weak-to-Strong Consistency for Enhanced Semi-Supervised Medical Image Segmentation—0
Semantic-Aware Contrastive Learning for Multi-object Medical Image Segmentation—0
Attention Mechanisms in Medical Image Segmentation: A Survey—0
Augmentation is AUtO-Net: Augmentation-Driven Contrastive Multiview Learning for Medical Image Segmentation—0
A Unified Framework for Generalized Low-Shot Medical Image Segmentation with Scarce Data—0
Auto-FedAvg: Learnable Federated Averaging for Multi-Institutional Medical Image Segmentation—0
Auto-FedRL: Federated Hyperparameter Optimization for Multi-institutional Medical Image Segmentation—0
Automated MRI Tumor Segmentation using hybrid U-Net with Transformer and Efficient Attention—0
Automatically Designing CNN Architectures for Medical Image Segmentation—0
Automatic Brain Tumour Segmentation and Biophysics-Guided Survival Prediction—0
Automatic Data Augmentation via Deep Reinforcement Learning for Effective Kidney Tumor Segmentation—0
Automatic Detection of Interplanetary Coronal Mass Ejections in Solar Wind In Situ Data—0
Automatic Localization of Deep Stimulation Electrodes Using Trajectory-based Segmentation Approach—0
Automatic Polyp Segmentation using U-Net-ResNet50—0
Automatic Rodent Brain MRI Lesion Segmentation with Fully Convolutional Networks—0
Automatic segmentation of meniscus based on MAE self-supervision and point-line weak supervision paradigm—0
Automatic Semantic Segmentation of the Lumbar Spine: Clinical Applicability in a Multi-parametric and Multi-centre Study on Magnetic Resonance Images—0
Autoregressive Medical Image Segmentation via Next-Scale Mask Prediction—0
Axial Attention Transformer Networks: A New Frontier in Breast Cancer Detection—0
BAGNet: Bidirectional Aware Guidance Network for Malignant Breast lesions Segmentation—0
Balancing Multi-Target Semi-Supervised Medical Image Segmentation with Collaborative Generalist and Specialists—0
BATseg: Boundary-aware Multiclass Spinal Cord Tumor Segmentation on 3D MRI Scans—0
BayeSeg: Bayesian Modeling for Medical Image Segmentation with Interpretable Generalizability—0
Bayesian Uncertainty Estimation by Hamiltonian Monte Carlo: Applications to Cardiac MRI Segmentation—0
BEFD: Boundary Enhancement and Feature Denoising for Vessel Segmentation—0
beta-FFT: Nonlinear Interpolation and Differentiated Training Strategies for Semi-Supervised Medical Image Segmentation—0
Beyond CNNs: Exploiting Further Inherent Symmetries in Medical Image Segmentation—0
Beyond Pixel Agreement: Large Language Models as Clinical Guardrails for Reliable Medical Image Segmentation—0
Beyond Pixel-Wise Supervision for Medical Image Segmentation: From Traditional Models to Foundation Models—0
Bidirectional RNN-based Few Shot Learning for 3D Medical Image Segmentation—0
Bidirectional Uncertainty-Aware Region Learning for Semi-Supervised Medical Image Segmentation—0
Bi-VLGM : Bi-Level Class-Severity-Aware Vision-Language Graph Matching for Text Guided Medical Image Segmentation—0
Boosting Medical Image Classification with Segmentation Foundation Model—0
Boosting Semi-Supervised Medical Image Segmentation via Masked Image Consistency and Discrepancy Learning—0
Boundary-aware Information Maximization for Self-supervised Medical Image Segmentation—0
End-to-End Boundary Aware Networks for Medical Image Segmentation—0
Boundary Distance Loss for Intra-/Extra-meatal Segmentation of Vestibular Schwannoma—0
Boundary feature fusion network for tooth image segmentation—0
BoundarySeg:An Embarrassingly Simple Method To Boost Medical Image Segmentation Performance for Low Data Regimes—0
Boundless Across Domains: A New Paradigm of Adaptive Feature and Cross-Attention for Domain Generalization in Medical Image Segmentation—0
Box-Adapt: Domain-Adaptive Medical Image Segmentation using Bounding BoxSupervision—0
Brain MR Image Segmentation in Small Dataset with Adversarial Defense and Task Reorganization—0
Brain MRI Segmentation using Rule-Based Hybrid Approach—0
BrainSegDMlF: A Dynamic Fusion-enhanced SAM for Brain Lesion Segmentation—0
Deep models for stroke segmentation: do complex architectures always perform better?—0
Brain Tumor Segmentation from MRI Images using Deep Learning Techniques—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