ArticleMachine learning in medical imaging. MLMI (Workshop)2014
Subject Specific Sparse Dictionary Learning for Atlas based Brain MRI Segmentation.
Article in Machine learning in medical imaging. MLMI (Workshop), 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed, 33 citations in OpenAlex.
- MTT and Blood-Brain Barrier Disruption within Asymptomatic Vascular WM Lesions.AJNR. American journal of neuroradiology · 2021Article
- In vivo estimates of axonal stretch and 3D brain deformation during mild head impact.Brain multiphysics · 2020Article
- Evaluating White Matter Lesion Segmentations with Refined Sørensen-Dice Analysis.Scientific reports · 2020Article
- Effect of disease-modifying therapies on subcortical gray matter atrophy in multiple sclerosis.Multiple sclerosis (Houndmills, Basingstoke, England) · 2020Observational
- Radiological images and machine learning: Trends, perspectives, and prospects.Computers in biology and medicine · 2019Review
- MIMoSA: An Automated Method for Intermodal Segmentation Analysis of Multiple Sclerosis Brain Lesions.Journal of neuroimaging : official journal of the American Society of Neuroimaging · 2018Article
- Multi-atlas segmentation of the hydrocephalus brain using an adaptive ventricle atlas.Proceedings of SPIE--the International Society for Optical Engineering · 2018Article
- Joint Intensity Fusion Image Synthesis Applied to Multiple Sclerosis Lesion Segmentation.Brainlesion : glioma, multiple sclerosis, stroke and traumatic brain injuries. BrainLes (Workshop) · 2018Article
- Whole Brain Parcellation with Pathology: Validation on Ventriculomegaly Patients.Patch-based techniques in medical imaging : third International Workshop, Patch-MI 2017, held in conjunction with MICCAI 2017, Quebec City, QC, Canada, September 14, 2017, Proceedings. Patch-MI (Workshop) (3rd : 2017 : Quebec, Quebec) · 2017Article
- Article
- Segmentation and labeling of the ventricular system in normal pressure hydrocephalus using patch-based tissue classification and multi-atlas labeling.Proceedings of SPIE--the International Society for Optical Engineering · 2016Article
- Automatic Region-Based Brain Classification of MRI-T1 Data.PloS one · 2016Article
- Subject-Specific Sparse Dictionary Learning for Atlas-Based Brain MRI Segmentation.IEEE journal of biomedical and health informatics · 2015Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
Quantitative measurements from segmentations of soft tissues from magnetic resonance images (MRI) of human brains provide important biomarkers for normal aging, as well as disease progression. In this paper, we propose a patch-based tissue classification method from MR images using sparse dictionary learning from an atlas. Unlike most atlas-based classification methods, deformable registration from the atlas to the subject is not required. An "atlas" consists of an MR image, its tissue probabilities, and the hard segmentation. The "subject" consists of the MR image and the corresponding affine registered atlas probabilities (or priors). 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. The same sparse combination is applied to the segmentation patches of the atlas to generate tissue memberships of the subject. The novel combination of prior probabilities in the example patches enables us to distinguish tissues having similar intensities but having different spatial location. We show that our method outperforms two state-of-the-art whole brain tissue segmentation methods. We experimented on 12 subjects having manual tissue delineations, obtaining mean Dice coefficients of 0:91 and 0:87 for cortical gray matter and cerebral white matter, respectively. In addition, experiments on subjects with ventriculomegaly shows significantly better segmentation using our approach than the competing methods.
Indexed as
Identifiers
What OpenQuestion holds
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.