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 101–150 of 2089 papers

TitleStatusHype
PRAD: Periapical Radiograph Analysis Dataset and Benchmark Model Development—0
Zeus: Zero-shot LLM Instruction for Union Segmentation in Multimodal Medical Imaging—0
HRMedSeg: Unlocking High-resolution Medical Image segmentation via Memory-efficient Attention ModelingCode1
Rethinking the Nested U-Net Approach: Enhancing Biomarker Segmentation with Attention Mechanisms and Multiscale Feature FusionCode0
Explaining Uncertainty in Multiple Sclerosis Lesion Segmentation Beyond Prediction Errors—0
Here Comes the Explanation: A Shapley Perspective on Multi-contrast Medical Image Segmentation—0
Performance Analysis of Deep Learning Models for Femur Segmentation in MRI Scan—0
Multi-encoder nnU-Net outperforms Transformer models with self-supervised pretraining—0
Multi-Granularity Vision Fastformer with Fusion Mechanism for Skin Lesion Segmentation—0
Agglomerating Large Vision Encoders via Distillation for VFSS Segmentation—0
SelfMedHPM: Self Pre-training With Hard Patches Mining Masked Autoencoders For Medical Image Segmentation—0
Test-time Adaptation for Foundation Medical Segmentation Model without Parametric Updates—0
STPNet: Scale-aware Text Prompt Network for Medical Image SegmentationCode1
Balancing Multi-Target Semi-Supervised Medical Image Segmentation with Collaborative Generalist and Specialists—0
Deconver: A Deconvolutional Network for Medical Image SegmentationCode0
WaveFormer: A 3D Transformer with Wavelet-Driven Feature Representation for Efficient Medical Image Segmentation—0
Federated Self-Supervised Learning for One-Shot Cross-Modal and Cross-Imaging Technique Segmentation—0
BiPVL-Seg: Bidirectional Progressive Vision-Language Fusion with Global-Local Alignment for Medical Image SegmentationCode1
MedCL: Learning Consistent Anatomy Distribution for Scribble-supervised Medical Image SegmentationCode0
Zero-shot Domain Generalization of Foundational Models for 3D Medical Image Segmentation: An Experimental Study—0
MedSegNet10: A Publicly Accessible Network Repository for Split Federated Medical Image Segmentation—0
Show and Segment: Universal Medical Image Segmentation via In-Context Learning—0
ASP-VMUNet: Atrous Shifted Parallel Vision Mamba U-Net for Skin Lesion SegmentationCode1
Optimization of MedSAM model based on bounding box adaptive perturbation algorithm—0
Prompt-Guided Dual-Path UNet with Mamba for Medical Image Segmentation—0
PSO-UNet: Particle Swarm-Optimized U-Net Framework for Precise Multimodal Brain Tumor Segmentation—0
ZECO: ZeroFusion Guided 3D MRI Conditional GenerationCode0
PG-SAM: Prior-Guided SAM with Medical for Multi-organ SegmentationCode1
Steady Progress Beats Stagnation: Mutual Aid of Foundation and Conventional Models in Mixed Domain Semi-Supervised Medical Image SegmentationCode1
An Attentive Representative Sample Selection Strategy Combined with Balanced Batch Training for Skin Lesion Segmentation—0
Downstream Analysis of Foundational Medical Vision Models for Disease Progression—0
MM-UNet: Meta Mamba UNet for Medical Image Segmentation—0
Cross-Modal Interactive Perception Network with Mamba for Lung Tumor Segmentation in PET-CT ImagesCode2
CausalCLIPSeg: Unlocking CLIP's Potential in Referring Medical Image Segmentation with Causal InterventionCode1
Attentional Triple-Encoder Network in Spatiospectral Domains for Medical Image Segmentation—0
SALT: Singular Value Adaptation with Low-Rank TransformationCode1
DEPT: Deep Extreme Point Tracing for Ultrasound Image Segmentation—0
Semi-KAN: KAN Provides an Effective Representation for Semi-Supervised Learning in Medical Image Segmentation—0
FedSCA: Federated Tuning with Similarity-guided Collaborative Aggregation for Heterogeneous Medical Image Segmentation—0
Organ-aware Multi-scale Medical Image Segmentation Using Text Prompt Engineering—0
RoMedFormer: A Rotary-Embedding Transformer Foundation Model for 3D Genito-Pelvic Structure Segmentation in MRI and CT—0
Multi-Prototype Embedding Refinement for Semi-Supervised Medical Image SegmentationCode1
Striving for Simplicity: Simple Yet Effective Prior-Aware Pseudo-Labeling for Semi-Supervised Ultrasound Image SegmentationCode0
Boosting Semi-Supervised Medical Image Segmentation via Masked Image Consistency and Discrepancy Learning—0
Test-Time Domain Generalization via Universe Learning: A Multi-Graph Matching Approach for Medical Image SegmentationCode2
Minding Fuzzy Regions: A Data-driven Alternating Learning Paradigm for Stable Lesion SegmentationCode0
SeqSAM: Autoregressive Multiple Hypothesis Prediction for Medical Image Segmentation using SAMCode0
GIGP: A Global Information Interacting and Geometric Priors Focusing Framework for Semi-supervised Medical Image Segmentation—0
QuickDraw: Fast Visualization, Analysis and Active Learning for Medical Image SegmentationCode0
Diff-CL: A Novel Cross Pseudo-Supervision Method for Semi-supervised 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
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