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 851–900 of 2089 papers

TitleStatusHype
Color-Quality Invariance for Robust Medical Image SegmentationCode0
Bidirectional Uncertainty-Aware Region Learning for Semi-Supervised Medical Image Segmentation—0
Is Long Range Sequential Modeling Necessary For Colorectal Tumor Segmentation?—0
A Comprehensive Review of U-Net and Its Variants: Advances and Applications in Medical Image Segmentation—0
A Novel Convolutional-Free Method for 3D Medical Imaging Segmentation—0
Synthetic Poisoning Attacks: The Impact of Poisoned MRI Image on U-Net Brain Tumor Segmentation—0
RFMedSAM 2: Automatic Prompt Refinement for Enhanced Volumetric Medical Image Segmentation with SAM 2—0
IMDPrompter: Adapting SAM to Image Manipulation Detection by Cross-View Automated Prompt Learning—0
Transfer Risk Map: Mitigating Pixel-level Negative Transfer in Medical Segmentation—0
Self-Prompt SAM: Medical Image Segmentation via Automatic Prompt SAM Adaptation—0
Distribution-aware Fairness Learning in Medical Image Segmentation From A Control-Theoretic PerspectiveCode0
CAD: Confidence-Aware Adaptive Displacement for Semi-Supervised Medical Image Segmentation—0
From Semantic Segmentation of Natural Images to Medical Image Segmentation Using ViT-Based Architectures—0
Medical Semantic Segmentation with Diffusion Pretrain—0
Post-Training Quantization for 3D Medical Image Segmentation: A Practical Study on Real Inference EnginesCode0
Point Cloud Upsampling as Statistical Shape Model for Pelvic—0
Efficient Knowledge Distillation of SAM for Medical Image Segmentation—0
LSU-Net: Lightweight Automatic Organs Segmentation Network For Medical Images—0
ENSeg: A Novel Dataset and Method for the Segmentation of Enteric Neuron Cells on Microscopy ImagesCode0
Enhancing Brain Tumor Segmentation Using Channel Attention and Transfer learningCode0
No More Sliding Window: Efficient 3D Medical Image Segmentation with Differentiable Top-k Patch Sampling—0
Few-Shot Adaptation of Training-Free Foundation Model for 3D Medical Image Segmentation—0
Deep Distance Map Regression Network with Shape-aware Loss for Imbalanced Medical Image Segmentation—0
UNetVL: Enhancing 3D Medical Image Segmentation with Chebyshev KAN Powered Vision-LSTMCode0
FedSemiDG: Domain Generalized Federated Semi-supervised Medical Image Segmentation—0
Imbalanced Medical Image Segmentation with Pixel-dependent Noisy LabelsCode0
PGP-SAM: Prototype-Guided Prompt Learning for Efficient Few-Shot Medical Image SegmentationCode0
Rethinking domain generalization in medical image segmentation: One image as one domain—0
CFFormer: Cross CNN-Transformer Channel Attention and Spatial Feature Fusion for Improved Segmentation of Low Quality Medical Images—0
PARF-Net: integrating pixel-wise adaptive receptive fields into hybrid Transformer-CNN network for medical image segmentation—0
GLoG-CSUnet: Enhancing Vision Transformers with Adaptable Radiomic Features for Medical Image SegmentationCode0
MedSegDiffNCA: Diffusion Models With Neural Cellular Automata for Skin Lesion Segmentation—0
Framework for lung CT image segmentation based on UNet++—0
tCURLoRA: Tensor CUR Decomposition Based Low-Rank Parameter Adaptation and Its Application in Medical Image Segmentation—0
EAUWSeg: Eliminating annotation uncertainty in weakly-supervised medical image segmentation—0
Tree-NET: Enhancing Medical Image Segmentation Through Efficient Low-Level Feature Training—0
nnY-Net: Swin-NeXt with Cross-Attention for 3D Medical Images Segmentation—0
A Semantic Knowledge Complementarity based Decoupling Framework for Semi-supervised Class-imbalanced Medical Image Segmentation—0
DyCON: Dynamic Uncertainty-aware Consistency and Contrastive Learning for Semi-supervised Medical Image Segmentation—0
Annotation Ambiguity Aware Semi-Supervised Medical Image Segmentation—0
The Impact Label Noise and Choice of Threshold has on Cross-Entropy and Soft-Dice in Image Segmentation—0
Take the Bull by the Horns: Learning to Segment Hard SamplesCode0
beta-FFT: Nonlinear Interpolation and Differentiated Training Strategies for Semi-Supervised Medical Image Segmentation—0
SegKAN: High-Resolution Medical Image Segmentation with Long-Distance DependenciesCode0
ProKAN: Progressive Stacking of Kolmogorov-Arnold Networks for Efficient Liver Segmentation—0
Neighbor Does Matter: Density-Aware Contrastive Learning for Medical Semi-supervised Segmentation—0
HELPNet: Hierarchical Perturbations Consistency and Entropy-guided Ensemble for Scribble Supervised Medical Image SegmentationCode0
Federated Learning with Partially Labeled Data: A Conditional Distillation Approach—0
PINN-EMFNet: PINN-based and Enhanced Multi-Scale Feature Fusion Network for Breast Ultrasound Images Segmentation—0
SGTC: Semantic-Guided Triplet Co-training for Sparsely Annotated Semi-Supervised Medical Image SegmentationCode0
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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