ArticleIEEE journal of biomedical and health informatics2015
Subject-Specific Sparse Dictionary Learning for Atlas-Based Brain MRI Segmentation.
Article in IEEE journal of biomedical and health informatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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Who cites it
33 citing papers in PubMed, 76 citations in OpenAlex.
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- Methodological challenges of measuring brain volumes and cortical thickness in idiopathic normal pressure hydrocephalus with a surface-based approach.Frontiers in neuroscience · 2024Article
- Unsupervised deep learning registration model for multimodal brain images.Journal of applied clinical medical physics · 2023Article
- An Efficient Optimization Approach for Glioma Tumor Segmentation in Brain MRI.Journal of digital imaging · 2022Article
- An atlas of classifiers-a machine learning paradigm for brain MRI segmentation.Medical & biological engineering & computing · 2021Article
- MTT and Blood-Brain Barrier Disruption within Asymptomatic Vascular WM Lesions.AJNR. American journal of neuroradiology · 2021Article
- Automatic brain lesion segmentation on standard magnetic resonance images: a scoping review.BMJ open · 2021Article
- Machine Learning Approaches in Study of Multiple Sclerosis Disease Through Magnetic Resonance Images.Frontiers in immunology · 2021Review
- Using Deep Learning in a Monocentric Study to Characterize Maternal Immune Environment for Predicting Pregnancy Outcomes in the Recurrent Reproductive Failure Patients.Frontiers in immunology · 2021Article
- MR Image-Based Attenuation Correction of Brain PET Imaging: Review of Literature on Machine Learning Approaches for Segmentation.Journal of digital imaging · 2020Review
- Sparse Data-Driven Learning for Effective and Efficient Biomedical Image Segmentation.Annual review of biomedical engineering · 2020Review
- Evaluating White Matter Lesion Segmentations with Refined Sørensen-Dice Analysis.Scientific reports · 2020Article
- TAPAS: A Thresholding Approach for Probability Map Automatic Segmentation in Multiple Sclerosis.NeuroImage. Clinical · 2020Article
- DeepHarmony: A deep learning approach to contrast harmonization across scanner changes.Magnetic resonance imaging · 2019Article
- Label fusion method combining pixel greyscale probability for brain MR segmentation.Scientific reports · 2019Article
- Quantitative vibratory sensation measurement is related to sensory cortical thickness in MS.Annals of clinical and translational neurology · 2019Article
- Article
- Brain ventricle parcellation using a deep neural network: Application to patients with ventriculomegaly.NeuroImage. Clinical · 2019Article
- Comparing fully automated state-of-the-art cerebellum parcellation from magnetic resonance images.NeuroImage · 2018Article
- Brain and retinal atrophy in African-Americans versus Caucasian-Americans with multiple sclerosis: a longitudinal study.Brain : a journal of neurology · 2018Article
Corrections and comments
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Authors and funding
7 authors at 4 institutions in 1 country.
Funding
Abstract
Quantitative measurements from segmentations of human brain magnetic resonance (MR) images provide important biomarkers for normal aging and disease progression. In this paper, we propose a patch-based tissue classification method from MR images that uses a sparse dictionary learning approach and atlas priors. Training data for the method consists of an atlas MR image, prior information maps depicting where different tissues are expected to be located, and a hard segmentation. Unlike most atlas-based classification methods that require deformable registration of the atlas priors to the subject, only affine registration is required between the subject and training atlas. A subject-specific patch dictionary is created by learning relevant patches from the atlas. Then the subject patches are modeled as sparse combinations of learned atlas patches leading to tissue memberships at each voxel. The combination of prior information in an example-based framework enables us to distinguish tissues having similar intensities but different spatial locations. We demonstrate the efficacy of the approach on the application of whole-brain tissue segmentation in subjects with healthy anatomy and normal pressure hydrocephalus, as well as lesion segmentation in multiple sclerosis patients. For each application, quantitative comparisons are made against publicly available state-of-the art approaches.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.