Papers MRI segmentation
“MRI segmentation” 태그가 달린 논문 177편 · 필터 해제
Weighted Mean Frequencies: a handcraft Fourier feature for 4D Flow MRI segmentation
In recent decades, the use of 4D Flow MRI images has enabled the quantification of velocity fields within a volume of interest and along the cardiac cycle. However, the lack of resolution and the presence of noise in the…
MRI segmentationSegmentationHypergraph Tversky-Aware Domain Incremental Learning for Brain Tumor Segmentation with Missing Modalities
Existing methods for multimodal MRI segmentation with missing modalities typically assume that all MRI modalities are available during training. However, in clinical practice, some modalities may be missing due to the se…
Brain Tumor SegmentationIncremental LearningMRI segmentationSegmentation+1SAMba-UNet: Synergizing SAM2 and Mamba in UNet with Heterogeneous Aggregation for Cardiac MRI Segmentation
To address the challenge of complex pathological feature extraction in automated cardiac MRI segmentation, this study proposes an innovative dual-encoder architecture named SAMba-UNet. The framework achieves cross-modal …
MambaMRI segmentationData-Agnostic Augmentations for Unknown Variations: Out-of-Distribution Generalisation in MRI Segmentation
Medical image segmentation models are often trained on curated datasets, leading to performance degradation when deployed in real-world clinical settings due to mismatches between training and test distributions. While d…
Data AugmentationImage SegmentationMedical Image SegmentationMRI segmentation+3Advances in Automated Fetal Brain MRI Segmentation and Biometry: Insights from the FeTA 2024 Challenge
Accurate fetal brain tissue segmentation and biometric analysis are essential for studying brain development in utero. The FeTA Challenge 2024 advanced automated fetal brain MRI analysis by introducing biometry predictio…
MRI segmentationSegmentationSuper-ResolutionPutting the Segment Anything Model to the Test with 3D Knee MRI - A Comparison with State-of-the-Art Performance
Menisci are cartilaginous tissue found within the knee that contribute to joint lubrication and weight dispersal. Damage to menisci can lead to onset and progression of knee osteoarthritis (OA), a condition that is a lea…
Image SegmentationMedical Image SegmentationMRI segmentationSegmentation+1Towards contrast- and pathology-agnostic clinical fetal brain MRI segmentation using SynthSeg
Magnetic resonance imaging (MRI) has played a crucial role in fetal neurodevelopmental research. Structural annotations of MR images are an important step for quantitative analysis of the developing human brain, with Dee…
Data AugmentationMRI segmentationHere Comes the Explanation: A Shapley Perspective on Multi-contrast Medical Image Segmentation
Deep learning has been successfully applied to medical image segmentation, enabling accurate identification of regions of interest such as organs and lesions. This approach works effectively across diverse datasets, incl…
Brain Tumor SegmentationImage SegmentationMedical Image SegmentationMRI segmentation+3Dynamically evolving segment anything model with continuous learning for medical image segmentation
Medical image segmentation is essential for clinical diagnosis, surgical planning, and treatment monitoring. Traditional approaches typically strive to tackle all medical image segmentation scenarios via one-time learnin…
DiversityImage SegmentationMedical Image SegmentationMRI segmentation+2Task-oriented Uncertainty Collaborative Learning for Label-Efficient Brain Tumor Segmentation
Multi-contrast magnetic resonance imaging (MRI) plays a vital role in brain tumor segmentation and diagnosis by leveraging complementary information from different contrasts. Each contrast highlights specific tumor chara…
Brain Tumor SegmentationMRI segmentationSegmentationSpecificity+1Deep Learning-Based Approach for Automatic 2D and 3D MRI Segmentation of Gliomas
Brain tumor diagnosis is a challenging task for clinicians in the modern world. Among the major reasons for cancer-related death is the brain tumor. Gliomas, a category of central nervous system (CNS) tumors, encompass d…
Computational EfficiencyMRI segmentationSegmentationSAMRI-2: A Memory-based Model for Cartilage and Meniscus Segmentation in 3D MRIs of the Knee Joint
Accurate morphometric assessment of cartilage-such as thickness/volume-via MRI is essential for monitoring knee osteoarthritis. Segmenting cartilage remains challenging and dependent on extensive expert-annotated dataset…
MRI segmentationSegmentationPathological MRI Segmentation by Synthetic Pathological Data Generation in Fetuses and Neonates
Developing new methods for the automated analysis of clinical fetal and neonatal MRI data is limited by the scarcity of annotated pathological datasets and privacy concerns that often restrict data sharing, hindering the…
Data AugmentationDiagnosticMRI segmentationSegmentationFull-Head Segmentation of MRI with Abnormal Brain Anatomy: Model and Data Release
The goal of this work was to develop a deep network for whole-head segmentation, including clinical MRIs with abnormal anatomy, and compile the first public benchmark dataset for this purpose. We collected 91 MRIs with v…
AnatomyBrain SegmentationMRI segmentationSegmentationNo More Sliding Window: Efficient 3D Medical Image Segmentation with Differentiable Top-k Patch Sampling
3D models surpass 2D models in CT/MRI segmentation by effectively capturing inter-slice relationships. However, the added depth dimension substantially increases memory consumption. While patch-based training alleviates …
CPUGPUImage SegmentationMedical Image Segmentation+3Rate-My-LoRA: Efficient and Adaptive Federated Model Tuning for Cardiac MRI Segmentation
Cardiovascular disease (CVD) and cardiac dyssynchrony are major public health problems in the United States. Precise cardiac image segmentation is crucial for extracting quantitative measures that help categorize cardiac…
Cardiac SegmentationFederated LearningImage SegmentationMRI segmentation+1A fuzzy rank-based ensemble of CNN models for MRI segmentation
Glioblastoma is the most common subtype of malignant tumors of the central nervous system. Segmentation of the brain tumor image is crucial for accelerating the diagnosis and treatment of a patient. In the paper, an adva…
Decision MakingMRI segmentationSegmentationTumor SegmentationVerSe: Integrating Multiple Queries as Prompts for Versatile Cardiac MRI Segmentation
Despite the advances in learning-based image segmentation approach, the accurate segmentation of cardiac structures from magnetic resonance imaging (MRI) remains a critical challenge. While existing automatic segmentatio…
Image SegmentationInteractive SegmentationMRI segmentationSegmentation+1QCResUNet: Joint Subject-level and Voxel-level Segmentation Quality Prediction
Deep learning has made significant strides in automated brain tumor segmentation from magnetic resonance imaging (MRI) scans in recent years. However, the reliability of these tools is hampered by the presence of poor-qu…
Brain Tumor SegmentationMRI segmentationSegmentationTumor SegmentationMRGen: Diffusion-based Controllable Data Engine for MRI Segmentation towards Unannotated Modalities
Medical image segmentation has recently demonstrated impressive progress with deep neural networks, yet the heterogeneous modalities and scarcity of mask annotations limit the development of segmentation models on unanno…
Image GenerationImage SegmentationMedical Image GenerationMedical Image Segmentation+3