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 1451–1500 of 2089 papers

TitleStatusHype
Feature Decoupling-Recycling Network for Fast Interactive Segmentation—0
Federated Contrastive Learning for Volumetric Medical Image Segmentation—0
Federated Data Model—0
Federated Learning with Partially Labeled Data: A Conditional Distillation Approach—0
Federated Self-Supervised Learning for One-Shot Cross-Modal and Cross-Imaging Technique Segmentation—0
Federated Semi-supervised Learning for Medical Image Segmentation with intra-client and inter-client Consistency—0
FedSCA: Federated Tuning with Similarity-guided Collaborative Aggregation for Heterogeneous Medical Image Segmentation—0
FedSemiDG: Domain Generalized Federated Semi-supervised Medical Image Segmentation—0
Feedback Chain Network For Hippocampus Segmentation—0
Few Exemplar-Based General Medical Image Segmentation via Domain-Aware Selective Adaptation—0
Few-Shot 3D Volumetric Segmentation with Multi-Surrogate Fusion—0
Few-Shot Adaptation of Training-Free Foundation Model for 3D Medical Image Segmentation—0
Few-shot brain segmentation from weakly labeled data with deep heteroscedastic multi-task networks—0
Few-shot image segmentation for cross-institution male pelvic organs using registration-assisted prototypical learning—0
Few-shot Medical Image Segmentation using a Global Correlation Network with Discriminative Embedding—0
Few-shot Medical Image Segmentation with Cycle-resemblance Attention—0
Few-Shot Medical Image Segmentation with Large Kernel Attention—0
Meta-learning with implicit gradients in a few-shot setting for medical image segmentation—0
FIAS: Feature Imbalance-Aware Medical Image Segmentation with Dynamic Fusion and Mixing Attention—0
FIESTA: Fourier-Based Semantic Augmentation with Uncertainty Guidance for Enhanced Domain Generalizability in Medical Image Segmentation—0
FIF-UNet: An Efficient UNet Using Feature Interaction and Fusion for Medical Image Segmentation—0
Finding the Most Transferable Tasks for Brain Image Segmentation—0
Fine-grained Recurrent Neural Networks for Automatic Prostate Segmentation in Ultrasound Images—0
FixMatchSeg: Fixing FixMatch for Semi-Supervised Semantic Segmentation—0
FlowSDF: Flow Matching for Medical Image Segmentation Using Distance Transforms—0
FNOSeg3D: Resolution-Robust 3D Image Segmentation with Fourier Neural Operator—0
Foundation AI Model for Medical Image Segmentation—0
FourierLoss: Shape-Aware Loss Function with Fourier Descriptors—0
FP-PET: Large Model, Multiple Loss And Focused Practice—0
Framework for lung CT image segmentation based on UNet++—0
FRCNet Frequency and Region Consistency for Semi-supervised Medical Image Segmentation—0
Freeze the backbones: A Parameter-Efficient Contrastive Approach to Robust Medical Vision-Language Pre-training—0
FreqU-FNet: Frequency-Aware U-Net for Imbalanced Medical Image Segmentation—0
From CNN to Transformer: A Review of Medical Image Segmentation Models—0
From Deterministic to Probabilistic: A Novel Perspective on Domain Generalization for Medical Image Segmentation—0
From Few to More: Scribble-based Medical Image Segmentation via Masked Context Modeling and Continuous Pseudo Labels—0
From Semantic Segmentation of Natural Images to Medical Image Segmentation Using ViT-Based Architectures—0
Fully Automatic Myocardial Segmentation of Contrast Echocardiography Sequence Using Random Forests Guided by Shape Model—0
Fully Convolutional Multi-scale Residual DenseNets for Cardiac Segmentation and Automated Cardiac Diagnosis using Ensemble of Classifiers—0
Fuse4Seg: Image-Level Fusion Based Multi-Modality Medical Image Segmentation—0
FUTURE-AI: Guiding Principles and Consensus Recommendations for Trustworthy Artificial Intelligence in Medical Imaging—0
FVP: Fourier Visual Prompting for Source-Free Unsupervised Domain Adaptation of Medical Image Segmentation—0
GASA-UNet: Global Axial Self-Attention U-Net for 3D Medical Image Segmentation—0
GASNet: Weakly-supervised Framework for COVID-19 Lesion Segmentation—0
Gaze2Segment: A Pilot Study for Integrating Eye-Tracking Technology into Medical Image Segmentation—0
GCL: Gradient-Guided Contrastive Learning for Medical Image Segmentation with Multi-Perspective Meta Labels—0
Generalization Capabilities of Neural Cellular Automata for Medical Image Segmentation: A Robust and Lightweight Approach—0
Generative Text-Guided 3D Vision-Language Pretraining for Unified Medical Image Segmentation—0
GIGP: A Global Information Interacting and Geometric Priors Focusing Framework for Semi-supervised Medical Image Segmentation—0
GLFNET: Global-Local (frequency) Filter Networks for efficient 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