Papers Skull Stripping
“Skull Stripping” 태그가 달린 논문 39편 · 필터 해제
BrainLesion Suite: A Flexible and User-Friendly Framework for Modular Brain Lesion Image Analysis
BrainLesion Suite is a versatile toolkit for building modular brain lesion image analysis pipelines in Python. Following Pythonic principles, BrainLesion Suite is designed to provide a 'brainless' development experience,…
Skull StrippingMindGrab for BrainChop: Fast and Accurate Skull Stripping for Command Line and Browser
We developed MindGrab, a parameter- and memory-efficient deep fully-convolutional model for volumetric skull-stripping in head images of any modality. Its architecture, informed by a spectral interpretation of dilated co…
Skull StrippingHow We Won the ISLES'24 Challenge by Preprocessing
Stroke is among the top three causes of death worldwide, and accurate identification of stroke lesion boundaries is critical for diagnosis and treatment. Supervised deep learning methods have emerged as the leading solut…
Lesion SegmentationSegmentationSkull StrippingSkull stripping with purely synthetic data
While many skull stripping algorithms have been developed for multi-modal and multi-species cases, there is still a lack of a fundamentally generalizable approach. We present PUMBA(PUrely synthetic Multimodal/species inv…
Image SegmentationMedical Image SegmentationSemantic SegmentationSkull StrippingDISARM++: Beyond scanner-free harmonization
Harmonization of T1-weighted MR images across different scanners is crucial for ensuring consistency in neuroimaging studies. This study introduces a novel approach to direct image harmonization, moving beyond feature st…
Image HarmonizationSkull StrippingPhaseGen: A Diffusion-Based Approach for Complex-Valued MRI Data Generation
Magnetic resonance imaging (MRI) raw data, or k-Space data, is complex-valued, containing both magnitude and phase information. However, clinical and existing Artificial Intelligence (AI)-based methods focus only on magn…
DiagnosticMRI ReconstructionSkull StrippingTumor SegmentationPfungst and Clever Hans: Identifying the unintended cues in a widely used Alzheimer's disease MRI dataset using explainable deep learning
Backgrounds. Deep neural networks have demonstrated high accuracy in classifying Alzheimer's disease (AD). This study aims to enlighten the underlying black-box nature and reveal individual contributions of T1-weighted (…
BinarizationSkull StrippingSpecificityUnsupervised Skull Segmentation via Contrastive MR-to-CT Modality Translation
The skull segmentation from CT scans can be seen as an already solved problem. However, in MR this task has a significantly greater complexity due to the presence of soft tissues rather than bones. Capturing the bone str…
Contrastive LearningSegmentationSkull StrippingSuper-ResolutionDomain Influence in MRI Medical Image Segmentation: spatial versus k-space inputs
Transformer-based networks applied to image patches have achieved cutting-edge performance in many vision tasks. However, lacking the built-in bias of convolutional neural networks (CNN) for local image statistics, they …
Image SegmentationMedical Image SegmentationSegmentationSemantic Segmentation+1Boosting Skull-Stripping Performance for Pediatric Brain Images
Skull-stripping is the removal of background and non-brain anatomical features from brain images. While many skull-stripping tools exist, few target pediatric populations. With the emergence of multi-institutional pediat…
Skull StrippingLongitudinal Volumetric Study for the Progression of Alzheimer's Disease from Structural MRI
Alzheimer's Disease (AD) is an irreversible neurodegenerative disorder affecting millions of individuals today. The prognosis of the disease solely depends on treating symptoms as they arise and proper caregiving, as the…
Alzheimer's DetectionImage RegistrationPrognosisSkull StrippingBrain MRI Segmentation using Template-Based Training and Visual Perception Augmentation
Deep learning models usually require sufficient training data to achieve high accuracy, but obtaining labeled data can be time-consuming and labor-intensive. Here we introduce a template-based training method to train a …
Brain SegmentationDeep LearningMRI segmentationSegmentation+1Neural Pre-Processing: A Learning Framework for End-to-end Brain MRI Pre-processing
Head MRI pre-processing involves converting raw images to an intensity-normalized, skull-stripped brain in a standard coordinate space. In this paper, we propose an end-to-end weakly supervised learning approach, called …
ReconstructionSkull StrippingWeakly-supervised LearningAttention-based convolutional neural network for perfusion T2-weighted MR images preprocessing
Accurate skull-stripping is crucial preprocessing in dynamic susceptibility contrast-enhanced perfusion magnetic resonance data analysis. The presence of non-brain tissues impacts the perfusion parameters assessment. In …
AnatomySkull StrippingTowards fully automated deep-learning-based brain tumor segmentation: is brain extraction still necessary?
State-of-the-art brain tumor segmentation is based on deep learning models applied to multi-modal MRIs. Currently, these models are trained on images after a preprocessing stage that involves registration, interpolation,…
Brain Tumor SegmentationSegmentationSkull StrippingTumor SegmentationPerformance Evaluation of Vanilla, Residual, and Dense 2D U-Net Architectures for Skull Stripping of Augmented 3D T1-weighted MRI Head Scans
Skull Stripping is a requisite preliminary step in most diagnostic neuroimaging applications. Manual Skull Stripping methods define the gold standard for the domain but are time-consuming and challenging to integrate int…
DiagnosticMRI segmentationSkull StrippingWide Range MRI Artifact Removal with Transformers
Artifacts on magnetic resonance scans are a serious challenge for both radiologists and computer-aided diagnosis systems. Most commonly, artifacts are caused by motion of the patients, but can also arise from device-spec…
DiagnosticSkull StrippingDetection and Classification of Brain tumors Using Deep Convolutional Neural Networks
Abnormal development of tissues in the body as a result of swelling and morbid enlargement is known as a tumor. They are mainly classified as Benign and Malignant. Tumour in the brain is fatal as it may be cancerous, so …
Data AugmentationDenoisingImage DenoisingSkull Stripping+1k-strip: A novel segmentation algorithm in k-space for the application of skull stripping
Objectives: Present a novel deep learning-based skull stripping algorithm for magnetic resonance imaging (MRI) that works directly in the information rich k-space. Materials and Methods: Using two datasets from different…
Skull StrippingNegligible effect of brain MRI data preprocessing for tumor segmentation
Magnetic resonance imaging (MRI) data is heterogeneous due to differences in device manufacturers, scanning protocols, and inter-subject variability. A conventional way to mitigate MR image heterogeneity is to apply prep…
AnatomyDenoisingImage DenoisingImage Segmentation+3