Papers Brain Image Segmentation
“Brain Image Segmentation” 태그가 달린 논문 35편 · 필터 해제
An Uncertainty-Aware Loss Function Incorporating Fuzzy Logic: Application to MRI Brain Image Segmentation
Accurate brain image segmentation, particularly for distinguishing various tissues from magnetic resonance imaging (MRI) images, plays a pivotal role in finding the neurological dis ease and medical image computing. In d…
Brain Image 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 SegmentationAddressing Annotation Scarcity in Hyperspectral Brain Image Segmentation with Unsupervised Domain Adaptation
This work presents a novel deep learning framework for segmenting cerebral vasculature in hyperspectral brain images. We address the critical challenge of severe label scarcity, which impedes conventional supervised trai…
Unsupervised Domain AdaptationBrain Image SegmentationSiNGR: Brain Tumor Segmentation via Signed Normalized Geodesic Transform Regression
One of the primary challenges in brain tumor segmentation arises from the uncertainty of voxels close to tumor boundaries. However, the conventional process of generating ground truth segmentation masks fails to treat su…
Brain Image SegmentationBrain Tumor SegmentationImage Segmentationregression+3Deep Learning-Based Brain Image Segmentation for Automated Tumour Detection
Introduction: The present study on the development and evaluation of an automated brain tumor segmentation technique based on deep learning using the 3D U-Net model. Objectives: The objective is to leverage state-of-the-…
Brain Image SegmentationBrain Tumor SegmentationDeep LearningImage Segmentation+3Transferring Ultrahigh-Field Representations for Intensity-Guided Brain Segmentation of Low-Field Magnetic Resonance Imaging
Ultrahigh-field (UHF) magnetic resonance imaging (MRI), i.e., 7T MRI, provides superior anatomical details of internal brain structures owing to its enhanced signal-to-noise ratio and susceptibility-induced contrast. How…
Brain Image SegmentationBrain SegmentationImage SegmentationSegmentation+1One-shot Joint Extraction, Registration and Segmentation of Neuroimaging Data
Brain extraction, registration and segmentation are indispensable preprocessing steps in neuroimaging studies. The aim is to extract the brain from raw imaging scans (i.e., extraction step), align it with a target brain …
Brain Image SegmentationImage RegistrationImage SegmentationMedical Image Registration+3Boosting multiple sclerosis lesion segmentation through attention mechanism
Magnetic resonance imaging is a fundamental tool to reach a diagnosis of multiple sclerosis and monitoring its progression. Although several attempts have been made to segment multiple sclerosis lesions using artificial …
Brain Image SegmentationLesion SegmentationMedical Image SegmentationThe ASNR-MICCAI Brain Tumor Segmentation (BraTS) Challenge 2023: Intracranial Meningioma
Meningiomas are the most common primary intracranial tumor in adults and can be associated with significant morbidity and mortality. Radiologists, neurosurgeons, neuro-oncologists, and radiation oncologists rely on multi…
Brain Image SegmentationBrain Tumor SegmentationMRI segmentationSegmentation+1Finding the Most Transferable Tasks for Brain Image Segmentation
Although many studies have successfully applied transfer learning to medical image segmentation, very few of them have investigated the selection strategy when multiple source tasks are available for transfer. In this pa…
Brain Image SegmentationImage SegmentationMedical Image SegmentationSegmentation+2ERNet: Unsupervised Collective Extraction and Registration in Neuroimaging Data
Brain extraction and registration are important preprocessing steps in neuroimaging data analysis, where the goal is to extract the brain regions from MRI scans (i.e., extraction step) and align them with a target brain …
Brain Image SegmentationImage RegistrationImage SegmentationMedical Image Registration+1Learning from imperfect training data using a robust loss function: application to brain image segmentation
Segmentation is one of the most important tasks in MRI medical image analysis and is often the first and the most critical step in many clinical applications. In brain MRI analysis, head segmentation is commonly used for…
Brain Image SegmentationEEGElectroencephalogram (EEG)Image Segmentation+3Subject-Specific Lesion Generation and Pseudo-Healthy Synthesis for Multiple Sclerosis Brain Images
Understanding the intensity characteristics of brain lesions is key for defining image-based biomarkers in neurological studies and for predicting disease burden and outcome. In this work, we present a novel foreground-b…
Brain Image SegmentationData AugmentationImage SegmentationSegmentation+1An Open-Source Tool for Longitudinal Whole-Brain and White Matter Lesion Segmentation
In this paper we describe and validate a longitudinal method for whole-brain segmentation of longitudinal MRI scans. It builds upon an existing whole-brain segmentation method that can handle multi-contrast data and robu…
3D Medical Imaging SegmentationBrain Image SegmentationBrain Lesion Segmentation From MriBrain Segmentation+3DAM-AL: Dilated Attention Mechanism with Attention Loss for 3D Infant Brain Image Segmentation
While Magnetic Resonance Imaging (MRI) has played an essential role in infant brain analysis, segmenting MRI into a number of tissues such as gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) is crucial …
Brain Image SegmentationImage SegmentationSemantic SegmentationAutomatic quality control framework for more reliable integration of machine learning-based image segmentation into medical workflows
Machine learning algorithms underpin modern diagnostic-aiding software, which has proved valuable in clinical practice, particularly in radiology. However, inaccuracies, mainly due to the limited availability of clinical…
Brain Image SegmentationDiagnosticImage SegmentationSegmentation+1Multi-Task Neural Processes
Neural processes have recently emerged as a class of powerful neural latent variable models that combine the strengths of neural networks and stochastic processes. As they can encode contextual data in the network's func…
Bayesian InferenceBrain Image SegmentationImage SegmentationInductive Bias+2EGMM: an Evidential Version of the Gaussian Mixture Model for Clustering
The Gaussian mixture model (GMM) provides a simple yet principled framework for clustering, with properties suitable for statistical inference. In this paper, we propose a new model-based clustering algorithm, called EGM…
Brain Image SegmentationClusteringImage SegmentationSemantic SegmentationA Longitudinal Method for Simultaneous Whole-Brain and Lesion Segmentation in Multiple Sclerosis
In this paper we propose a novel method for the segmentation of longitudinal brain MRI scans of patients suffering from Multiple Sclerosis. The method builds upon an existing cross-sectional method for simultaneous whole…
3D Medical Imaging SegmentationBrain Image SegmentationBrain Lesion Segmentation From MriBrain Segmentation+3A Contrast-Adaptive Method for Simultaneous Whole-Brain and Lesion Segmentation in Multiple Sclerosis
Here we present a method for the simultaneous segmentation of white matter lesions and normal-appearing neuroanatomical structures from multi-contrast brain MRI scans of multiple sclerosis patients. The method integrates…
3D Medical Imaging SegmentationBrain Image SegmentationBrain Lesion Segmentation From MriBrain Segmentation+3