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

Showing 1101–1150 of 2089 papers

TitleStatusHype
A Lightweight U-like Network Utilizing Neural Memory Ordinary Differential Equations for Slimming the DecoderCode0
ZScribbleSeg: Zen and the Art of Scribble Supervised Medical Image Segmentation—0
Machine Vision Guided 3D Medical Image Compression for Efficient Transmission and Accurate Segmentation in the Clouds—0
Generative Model-Based Ischemic Stroke Lesion Segmentation—0
C2FNAS: Coarse-to-Fine Neural Architecture Search for 3D Medical Image Segmentation—0
Distribution-Aware Replay for Continual MRI Segmentation—0
MedUHIP: Towards Human-In-the-Loop Medical Segmentation—0
Rethinking the Mean Teacher Strategy from the Perspective of Self-paced Learning—0
DM-SegNet: Dual-Mamba Architecture for 3D Medical Image Segmentation with Global Context Modeling—0
Generalist Models in Medical Image Segmentation: A Survey and Performance Comparison with Task-Specific Approaches—0
Med-URWKV: Pure RWKV With ImageNet Pre-training For Medical Image Segmentation—0
3D Deep Affine-Invariant Shape Learning for Brain MR Image Segmentation—0
3D Densely Convolutional Networks for VolumetricSegmentation—0
3D medical image segmentation with labeled and unlabeled data using autoencoders at the example of liver segmentation in CT images—0
3D Medical Imaging Segmentation on Non-Contrast CT—0
3D Segmentation with Exponential Logarithmic Loss for Highly Unbalanced Object Sizes—0
3D Semi-Supervised Learning with Uncertainty-Aware Multi-View Co-Training—0
4D Multi-atlas Label Fusion using Longitudinal Images—0
A 2D dilated residual U-Net for multi-organ segmentation in thoracic CT—0
A 3D Coarse-to-Fine Framework for Volumetric Medical Image Segmentation—0
A Cascaded Deep-Learning Framework for Segmentation of Metastatic Brain Tumors Before and After Stereotactic Radiation Therapy—0
Accelerating Diffusion Models via Pre-segmentation Diffusion Sampling for Medical Image Segmentation—0
Computer-Vision Benchmark Segment-Anything Model (SAM) in Medical Images: Accuracy in 12 Datasets—0
A Chebyshev Confidence Guided Source-Free Domain Adaptation Framework for Medical Image Segmentation—0
A Classifier-Free Incremental Learning Framework for Scalable Medical Image Segmentation—0
A Comprehensive Review of U-Net and Its Variants: Advances and Applications in Medical Image Segmentation—0
A Comprehensive Study on Medical Image Segmentation using Deep Neural Networks—0
ODES: Domain Adaptation with Expert Guidance for Online Medical Image Segmentation—0
ACT: Semi-supervised Domain-adaptive Medical Image Segmentation with Asymmetric Co-training—0
Adapting Off-the-Shelf Source Segmenter for Target Medical Image Segmentation—0
Adaptive Adversarial Training to Improve Adversarial Robustness of DNNs for Medical Image Segmentation and Detection—0
Adaptive Affinity-Based Generalization For MRI Imaging Segmentation Across Resource-Limited Settings—0
Adaptive Mix for Semi-Supervised Medical Image Segmentation—0
Adaptively Weighted Data Augmentation Consistency Regularization for Robust Optimization under Concept Shift—0
Addressing Class Imbalance in Semi-supervised Image Segmentation: A Study on Cardiac MRI—0
A-DenseUNet: Adaptive Densely Connected UNet for Polyp Segmentation in Colonoscopy Images with Atrous Convolution—0
A Divide-and-Conquer Approach towards Understanding Deep Networks—0
Advancing 3D Medical Image Segmentation: Unleashing the Potential of Planarian Neural Networks in Artificial Intelligence—0
Advancing Medical Image Segmentation with Mini-Net: A Lightweight Solution Tailored for Efficient Segmentation of Medical Images—0
Advancing Volumetric Medical Image Segmentation via Global-Local Masked Autoencoder—0
Adversarial Attack Driven Data Augmentation for Accurate And Robust Medical Image Segmentation—0
Adversarial Consistency for Single Domain Generalization in Medical Image Segmentation—0
A dynamic interactive learning framework for automated 3D medical image segmentation—0
A Characteristic Function-based Algorithm for Geodesic Active Contours—0
A Fast, Semi-Automatic Brain Structure Segmentation Algorithm for Magnetic Resonance Imaging—0
Affinity-Graph-Guided Contractive Learning for Pretext-Free Medical Image Segmentation with Minimal Annotation—0
AFTer-UNet: Axial Fusion Transformer UNet for Medical Image Segmentation—0
Agglomerating Large Vision Encoders via Distillation for VFSS Segmentation—0
A hybrid approach based segmentation technique for brain tumor in MRI Images—0
A hybrid approach for improving U-Net variants in medical image segmentation—0
Show:102550
← PrevPage 23 of 42Next →

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
4RaBiTmean Dice0.95—Unverified
5Yolo-SAM 2mean 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