Papers Brain Segmentation
“Brain Segmentation” 태그가 달린 논문 163편 · 필터 해제
Decoupling Parcellation from Classification: Systematic Benchmark of Fast Brain Segmentation Methods for Alzheimer's Disease Detection
Brain parcellation and classification are typically evaluated in isolation, yet downstream AD detection performance depends on their interaction. We decouple these components and systematically benchmark fast deep learni…
Alzheimer's Disease DetectionBrain SegmentationTowards Reliable and Reproducible Fetal Brain Biometry: A Deep Learning Approach Using MRI
Fetal brain biometry is essential for quantitative assessment of brain development, supporting gestational age estimation, developmental monitoring, and detection of abnormalities. In clinical practice, measurements are …
Brain SegmentationAge EstimationULF-Synth: Physics-Guided Ultra-Low-Field MRI Enhancement for Pediatric Neuroimaging
Ultra-low-field (ULF) MRI offers portable and accessible neuroimaging but suffers from reduced signal-to-noise ratio and limited spatial resolution compared to high-field (HF) systems. Acquiring paired ULF-HF data for su…
Brain SegmentationAutomatic Landmark-Based Segmentation of Human Subcortical Structures in MRI
Precise segmentation of brain structures in magnetic resonance imaging (MRI) is essential for reliable neuroimaging analysis, yet voxel-wise deep models often yield anatomically inconsistent results that diverge from exp…
Semantic SegmentationBrain SegmentationExploring Entropy-based Active Learning for Fair Brain Segmentation
Active learning (AL) has emerged as a crucial strategy for reducing the prohibitive costs associated with medical image segmentation. However, standard uncertainty-based AL methods typically focus on maximizing performan…
Medical Image SegmentationBrain SegmentationActive Learning4DLoG: Generative Modeling of Neurodegenerative Brain Anatomy with 4D Longitudinal Diffusion Model
Modeling and predicting neurodegenerative disease progression from medical images remains a major challenge in medical AI, with significant implications for early diagnosis, disease monitoring, and treatment planning. Ho…
Brain SegmentationA Novel Framework using Intuitionistic Fuzzy Logic with U-Net and U-Net++ Architecture: A case Study of MRI Bain Image Segmentation
Accurate segmentation of brain images from magnetic resonance imaging (MRI) scans plays a pivotal role in brain image analysis and the diagnosis of neurological disorders. Deep learning algorithms, particularly U-Net and…
Brain SegmentationImage SegmentationAnatomy-Preserving Latent Diffusion for Generation of Brain Segmentation Masks with Ischemic Infarct
The scarcity of high-quality segmentation masks remains a major bottleneck for medical image analysis, particularly in non-contrast CT (NCCT) neuroimaging, where manual annotation is costly and variable. To address this …
Brain SegmentationAn Intuitionistic Fuzzy Logic Driven UNet architecture: Application to Brain Image segmentation
Accurate segmentation of MRI brain images is essential for image analysis, diagnosis of neuro-logical disorders and medical image computing. In the deep learning approach, the convolutional neural networks (CNNs), especi…
Medical Image SegmentationBrain Image SegmentationBrain SegmentationCortex-Grounded Diffusion Models for Brain Image Generation
Synthetic neuroimaging data can mitigate critical limitations of real-world datasets, including the scarcity of rare phenotypes, domain shifts across scanners, and insufficient longitudinal coverage. However, existing ge…
Brain SegmentationImage GenerationGOUHFI 2.0: A Next-Generation Toolbox for Brain Segmentation and Cortex Parcellation at Ultra-High Field MRI
Ultra-High Field MRI (UHF-MRI) is increasingly used in large-scale neuroimaging studies, yet automatic brain segmentation and cortical parcellation remain challenging due to signal inhomogeneities, heterogeneous contrast…
Brain SegmentationDeep infant brain segmentation from multi-contrast MRI
Segmentation of magnetic resonance images (MRI) facilitates analysis of human brain development by delineating anatomical structures. However, in infants and young children, accurate segmentation is challenging due to de…
Brain SegmentationRewiring Development in Brain Segmentation: Leveraging Adult Brain Priors for Enhancing Infant MRI Segmentation
Accurate segmentation of infant brain MRI is critical for studying early neurodevelopment and diagnosing neurological disorders. Yet, it remains a fundamental challenge due to continuously evolving anatomy of the subject…
Brain SegmentationTransfer LearningDomain AdaptationMSD-KMamba: Bidirectional Spatial-Aware Multi-Modal 3D Brain Segmentation via Multi-scale Self-Distilled Fusion Strategy
Numerous CNN-Transformer hybrid models rely on high-complexity global attention mechanisms to capture long-range dependencies, which introduces non-linear computational complexity and leads to significant resource consum…
Computational EfficiencyKnowledge DistillationBrain SegmentationImage SegmentationEnhancing Corpus Callosum Segmentation in Fetal MRI via Pathology-Informed Domain Randomization
Accurate fetal brain segmentation is crucial for extracting biomarkers and assessing neurodevelopment, especially in conditions such as corpus callosum dysgenesis (CCD), which can induce drastic anatomical changes. Howev…
Synthetic Data GenerationBrain SegmentationREFLECT: Rectified Flows for Efficient Brain Anomaly Correction Transport
Unsupervised anomaly detection (UAD) in brain imaging is crucial for identifying pathologies without the need for labeled data. However, accurately localizing anomalies remains challenging due to the intricate structure …
Unsupervised Anomaly DetectionBrain SegmentationEnhancing and Accelerating Brain MRI through Deep Learning Reconstruction Using Prior Subject-Specific Imaging
Magnetic resonance imaging (MRI) is a crucial medical imaging modality. However, long acquisition times remain a significant challenge, leading to increased costs, and reduced patient comfort. Recent studies have shown t…
Image ReconstructionBrain SegmentationMRI ReconstructionGOUHFI: a novel contrast- and resolution-agnostic segmentation tool for Ultra-High Field MRI
Recently, Ultra-High Field MRI (UHF-MRI) has become more available and one of the best tools to study the brain. One common step in quantitative neuroimaging is the brain segmentation. However, the differences between UH…
Brain SegmentationSegmentationFLAIRBrainSeg: Fine-grained brain segmentation using FLAIR MRI only
This paper introduces a novel method for brain segmentation using only FLAIR MRIs, specifically targeting cases where access to other imaging modalities is limited. By leveraging existing automatic segmentation methods, …
Brain SegmentationImage GenerationSegmentationExploring Robustness of Cortical Morphometry in the presence of white matter lesions, using Diffusion Models for Lesion Filling
Cortical thickness measurements from magnetic resonance imaging, an important biomarker in many neurodegenerative and neurological disorders, are derived by many tools from an initial voxel-wise tissue segmentation. Whit…
Brain SegmentationDeep LearningDenoisingSegmentation