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 151–200 of 2089 papers

TitleStatusHype
3D Medical Imaging Segmentation on Non-Contrast CT—0
QuantU-Net: Efficient Wearable Medical Imaging Using Bitwidth as a Trainable Parameter—0
Task-Specific Knowledge Distillation from the Vision Foundation Model for Enhanced Medical Image Segmentation—0
Semi-Supervised Medical Image Segmentation via Knowledge Mining from Large Models—0
Continuous Online Adaptation Driven by User Interaction for Medical Image Segmentation—0
DiffAtlas: GenAI-fying Atlas Segmentation via Image-Mask DiffusionCode2
Dynamically evolving segment anything model with continuous learning for medical image segmentation—0
Partially Supervised Unpaired Multi-Modal Learning for Label-Efficient Medical Image Segmentation—0
Gaussian Random Fields as an Abstract Representation of Patient Metadata for Multimodal Medical Image SegmentationCode0
We Care Each Pixel: Calibrating on Medical Segmentation ModelCode0
Enhancing SAM with Efficient Prompting and Preference Optimization for Semi-supervised Medical Image Segmentation—0
WeakMedSAM: Weakly-Supervised Medical Image Segmentation via SAM with Sub-Class Exploration and Prompt Affinity MiningCode1
GBT-SAM: Adapting a Foundational Deep Learning Model for Generalizable Brain Tumor Segmentation via Efficient Integration of Multi-Parametric MRI DataCode1
Rethinking Few-Shot Medical Image Segmentation by SAM2: A Training-Free Framework with Augmentative Prompting and Dynamic Matching—0
Implicit U-KAN2.0: Dynamic, Efficient and Interpretable Medical Image Segmentation—0
Federated nnU-Net for Privacy-Preserving Medical Image SegmentationCode1
Primus: Enforcing Attention Usage for 3D Medical Image Segmentation—0
From Claims to Evidence: A Unified Framework and Critical Analysis of CNN vs. Transformer vs. Mamba in Medical Image SegmentationCode1
SparseMamba-PCL: Scribble-Supervised Medical Image Segmentation via SAM-Guided Progressive Collaborative LearningCode0
UniNet: A Contrastive Learning-guided Unified Framework with Feature Selection for Anomaly DetectionCode2
Autoregressive Medical Image Segmentation via Next-Scale Mask Prediction—0
SemiSAM+: Rethinking Semi-Supervised Medical Image Segmentation in the Era of Foundation ModelsCode2
Style Content Decomposition-based Data Augmentation for Domain Generalizable Medical Image SegmentationCode0
Test-Time Modality Generalization for Medical Image Segmentation—0
VesselSAM: Leveraging SAM for Aortic Vessel Segmentation with AtrousLoRACode1
M3DA: Benchmark for Unsupervised Domain Adaptation in 3D Medical Image SegmentationCode0
Image Translation-Based Unsupervised Cross-Modality Domain Adaptation for Medical Image Segmentation—0
Vision Foundation Models in Medical Image Analysis: Advances and Challenges—0
MGFI-Net: A Multi-Grained Feature Integration Network for Enhanced Medical Image Segmentation—0
WRT-SAM: Foundation Model-Driven Segmentation for Generalized Weld Radiographic Testing—0
Leveraging Labelled Data Knowledge: A Cooperative Rectification Learning Network for Semi-supervised 3D Medical Image SegmentationCode1
RemInD: Remembering Anatomical Variations for Interpretable Domain Adaptive Medical Image Segmentation—0
QMaxViT-Unet+: A Query-Based MaxViT-Unet with Edge Enhancement for Scribble-Supervised Segmentation of Medical ImagesCode1
Hi-End-MAE: Hierarchical encoder-driven masked autoencoders are stronger vision learners for medical image segmentationCode1
Improving Lesion Segmentation in Medical Images by Global and Regional Feature Compensation—0
Bidirectional Uncertainty-Aware Region Learning for Semi-Supervised Medical Image Segmentation—0
Color-Quality Invariance for Robust Medical Image SegmentationCode0
Is Long Range Sequential Modeling Necessary For Colorectal Tumor Segmentation?—0
Conditional diffusion model with spatial attention and latent embedding for medical image segmentationCode1
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
UD-Mamba: A pixel-level uncertainty-driven Mamba model for medical image segmentationCode1
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
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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