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 2001–2050 of 2089 papers

TitleStatusHype
Transfer Risk Map: Mitigating Pixel-level Negative Transfer in Medical Segmentation—0
Transformation Consistent Self-ensembling Model for Semi-supervised Medical Image Segmentation—0
Transformer-CNN Fused Architecture for Enhanced Skin Lesion Segmentation—0
Transformer-Unet: Raw Image Processing with Unet—0
Transformer Utilization in Medical Image Segmentation Networks—0
Transforming medical imaging with Transformers? A comparative review of key properties, current progresses, and future perspectives—0
TransFusion: Multi-view Divergent Fusion for Medical Image Segmentation with Transformers—0
TranSiam: Fusing Multimodal Visual Features Using Transformer for Medical Image Segmentation—0
TransMedSeg: A Transferable Semantic Framework for Semi-Supervised Medical Image Segmentation—0
Tree-NET: Enhancing Medical Image Segmentation Through Efficient Low-Level Feature Training—0
TricycleGAN: Unsupervised Image Synthesis and Segmentation Based on Shape Priors—0
Trilateral Attention Network for Real-time Medical Image Segmentation—0
Tri-Plane Mamba: Efficiently Adapting Segment Anything Model for 3D Medical Images—0
Trust, but Verify: Robust Image Segmentation using Deep Learning—0
Trusting Semantic Segmentation Networks—0
Trustworthy Deep Learning for Medical Image Segmentation—0
SUPER-Net: Trustworthy Medical Image Segmentation with Uncertainty Propagation in Encoder-Decoder Networks—0
TSUBF-Net: Trans-Spatial UNet-like Network with Bi-direction Fusion for Segmentation of Adenoid Hypertrophy in CT—0
Tumor-Centered Patching for Enhanced Medical Image Segmentation—0
Two layer Ensemble of Deep Learning Models for Medical Image Segmentation—0
Two-stage Joint Transductive and Inductive learning for Nuclei Segmentation—0
Two-Stage Multi-task Self-Supervised Learning for Medical Image Segmentation—0
Two-Stream UNET Networks for Semantic Segmentation in Medical Images—0
UGCANet: A Unified Global Context-Aware Transformer-based Network with Feature Alignment for Endoscopic Image Analysis—0
UGformer for Robust Left Atrium and Scar Segmentation Across Scanners—0
Ultrasound Image Segmentation of Thyroid Nodule via Latent Semantic Feature Co-Registration—0
Challenge Summary U-MedSAM: Uncertainty-aware MedSAM for Medical Image Segmentation—0
Uncertainty-Aware Adapter: Adapting Segment Anything Model (SAM) for Ambiguous Medical Image Segmentation—0
Bayesian Neural Networks for 2D MRI Segmentation—0
Uncertainty-Aware Deep Co-training for Semi-supervised Medical Image Segmentation—0
Uncertainty-aware Evidential Fusion-based Learning for Semi-supervised Medical Image Segmentation—0
Uncertainty-aware Multi-modal Learning via Cross-modal Random Network Prediction—0
Uncertainty-aware multi-view co-training for semi-supervised medical image segmentation and domain adaptation—0
Uncertainty-Aware Training of Neural Networks for Selective Medical Image Segmentation—0
Uncertainty-Guided Mutual Consistency Learning for Semi-Supervised Medical Image Segmentation—0
Uncertainty guided semi-supervised segmentation of retinal layers in OCT images—0
Uncertainty Modeling in Ultrasound Image Segmentation for Precise Fetal Biometric Measurements—0
Uncertainty Quantification in Medical Image Segmentation with Multi-decoder U-Net—0
Deep Learning with Uncertainty Quantification for Predicting the Segmentation Dice Coefficient of Prostate Cancer Biopsy Images—0
UNet-2022: Exploring Dynamics in Non-isomorphic Architecture—0
UNet-3D with Adaptive TverskyCE Loss for Pancreas Medical Image Segmentation—0
U-Net and its variants for medical image segmentation: theory and applications—0
U-Net and its variants for Medical Image Segmentation : A short review—0
UNet#: A UNet-like Redesigning Skip Connections for Medical Image Segmentation—0
U-Net Based Architecture for an Improved Multiresolution Segmentation in Medical Images—0
U-Net in Medical Image Segmentation: A Review of Its Applications Across Modalities—0
U-Netmer: U-Net meets Transformer for medical image segmentation—0
U-Net Using Stacked Dilated Convolutions for Medical Image Segmentation—0
Unified HT-CNNs Architecture: Transfer Learning for Segmenting Diverse Brain Tumors in MRI from Gliomas to Pediatric Tumors—0
Unified Medical Image Segmentation with State Space Modeling Snake—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
9SegMedmean Dice0.95—Unverified
10DuATmean 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
9Automatic AI-SAMAvg DSC92.06—Unverified
10nnFormerAvg DSC92.06—Unverified
#ModelMetricClaimedVerifiedStatus
1StardistF184.6—Unverified