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 1501–1550 of 2089 papers

TitleStatusHype
Global Guidance Network for Breast Lesion Segmentation in Ultrasound Images—0
Global-Local Medical SAM Adaptor Based on Full Adaption—0
Globally Optimal Segmentation of Mutually Interacting Surfaces using Deep Learning—0
GMISeg: General Medical Image Segmentation without Re-Training—0
Graph-BAS3Net: Boundary-Aware Semi-Supervised Segmentation Network With Bilateral Graph Convolution—0
GraphCL: Graph-based Clustering for Semi-Supervised Medical Image Segmentation—0
Group Shift Pointwise Convolution for Volumetric Medical Image Segmentation—0
Harmonized Spatial and Spectral Learning for Robust and Generalized Medical Image Segmentation—0
HC-Mamba: Vision MAMBA with Hybrid Convolutional Techniques for Medical Image Segmentation—0
HDC: Hierarchical Distillation for Multi-level Noisy Consistency in Semi-Supervised Fetal Ultrasound Segmentation—0
Heed the Noise in Performance Evaluations in Neural Architecture Search—0
Here Comes the Explanation: A Shapley Perspective on Multi-contrast Medical Image Segmentation—0
hidden markov random fields and cuckoo search method for medical image segmentation—0
Hierarchical Attention Networks for Medical Image Segmentation—0
Hierarchical Self-Prompting SAM: A Prompt-Free Medical Image Segmentation Framework—0
Hierarchical Self-Supervised Learning for Medical Image Segmentation Based on Multi-Domain Data Aggregation—0
Hierarchical Uncertainty Estimation for Medical Image Segmentation Networks—0
High-level Prior-based Loss Functions for Medical Image Segmentation: A Survey—0
High-Resolution Boundary Detection for Medical Image Segmentation with Piece-Wise Two-Sample T-Test Augmented Loss—0
HNOSeg-XS: Extremely Small Hartley Neural Operator for Efficient and Resolution-Robust 3D Image Segmentation—0
Holographic Visualisation of Radiology Data and Automated Machine Learning-based Medical Image Segmentation—0
How Can CNNs Use Image Position for Segmentation?—0
Uncertainty categories in medical image segmentation: a study of source-related diversity—0
How good nnU-Net for Segmenting Cardiac MRI: A Comprehensive Evaluation—0
How inter-rater variability relates to aleatoric and epistemic uncertainty: a case study with deep learning-based paraspinal muscle segmentation—0
How precise are performance estimates for typical medical image segmentation tasks?—0
How to select slices for annotation to train best-performing deep learning segmentation models for cross-sectional medical images?—0
HResFormer: Hybrid Residual Transformer for Volumetric Medical Image Segmentation—0
HST-MRF: Heterogeneous Swin Transformer with Multi-Receptive Field for Medical Image Segmentation—0
HyperSegNAS: Bridging One-Shot Neural Architecture Search with 3D Medical Image Segmentation using HyperNet—0
ICMSC: Intra- and Cross-modality Semantic Consistency for Unsupervised Domain Adaptation on Hip Joint Bone Segmentation—0
_1DecNet+: A new architecture framework by _1 decomposition and iteration unfolding for sparse feature segmentation—0
Image-level supervision and self-training for transformer-based cross-modality tumor segmentation—0
Image Segmentation Using Hybrid Representations—0
Image Translation-Based Unsupervised Cross-Modality Domain Adaptation for Medical Image Segmentation—0
IMDPrompter: Adapting SAM to Image Manipulation Detection by Cross-View Automated Prompt Learning—0
Impact of individual rater style on deep learning uncertainty in medical imaging segmentation—0
Implementation of a Modified U-Net for Medical Image Segmentation on Edge Devices—0
Implicit U-KAN2.0: Dynamic, Efficient and Interpretable Medical Image Segmentation—0
Implicit U-Net for volumetric medical image segmentation—0
Improved cell segmentation using deep learning in label-free optical microscopy images—0
Improved Inference via Deep Input Transfer—0
Improved Unet brain tumor image segmentation based on GSConv module and ECA attention mechanism—0
Improving CT Image Segmentation Accuracy Using StyleGAN Driven Data Augmentation—0
Improving Lesion Segmentation in Medical Images by Global and Regional Feature Compensation—0
Improving Segment Anything on the Fly: Auxiliary Online Learning and Adaptive Fusion for Medical Image Segmentation—0
Improving Uncertainty-based Out-of-Distribution Detection for Medical Image Segmentation—0
In-context learning for medical image segmentation—0
Incorporating prior knowledge in medical image segmentation: a survey—0
Incremental Learning Meets Transfer Learning: Application to Multi-site Prostate MRI 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