Subject-Specific Sparse Dictionary Learning for Atlas-Based Brain MRI Segmentation

IEEE Journal of Biomedical and Health Informatics
Snehashis RoyDzung L Pham

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 s...Continue Reading

References

Jan 13, 1998·Vision Research·B A Olshausen, D J Field
Jan 11, 2000·IEEE Transactions on Medical Imaging·K Van LeemputP Suetens
Aug 23, 2001·Medical Image Analysis·M Jenkinson, S Smith
Feb 15, 2003·IEEE Transactions on Medical Imaging·Alex P ZijdenbosAlan C Evans
Jul 15, 2004·IEEE Transactions on Medical Imaging·Simon K WarfieldWilliam M Wells
Jun 16, 2005·NeuroImage·John Ashburner, Karl J Friston
Jul 22, 2008·Medical Image Analysis·Pierre-Louis Bazin, Dzung L Pham
Feb 4, 2010·Proceedings·Snehashis RoyJerry L Prince
Jan 1, 2008·Proceedings·Snehashis RoyJerry L Prince
Apr 10, 2010·IEEE Transactions on Medical Imaging·Nicholas J TustisonJames C Gee
May 1, 2010·Proceedings - Society of Photo-Optical Instrumentation Engineers·Snehashis RoyJerry L Prince
Jun 22, 2010·IEEE Transactions on Medical Imaging·Mert R SabuncuPolina Golland
Feb 15, 2011·NeuroImage·M Jorge CardosoUNKNOWN Alzheimer's Disease Neuroimaging Initiative
Apr 5, 2011·NeuroImage·Aaron CarassJerry L Prince
Nov 19, 2011·NeuroImage·Pierrick CoupéUNKNOWN Alzheimer's disease Neuroimaging Initiative
Jan 1, 1980·IEEE Transactions on Pattern Analysis and Machine Intelligence·J C Bezdek
Jun 27, 2012·IEEE Transactions on Pattern Analysis and Machine Intelligence·Hongzhi WangPaul A Yushkevich
Mar 26, 2013·NeuroImage·Tong TongUNKNOWN Alzheimer's Disease Neuroimaging Initiative
Sep 24, 2013·IEEE Transactions on Medical Imaging·Snehashis RoyJerry L Prince
Jan 21, 2014·Proceedings·Amod JogJerry L Prince
Nov 11, 2014·Machine Learning in Medical Imaging·Snehashis RoyDzung L Pham

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Citations

Apr 17, 2015·Current Opinion in Neurobiology·Simon PeronKarel Svoboda
Sep 7, 2016·Medical Image Analysis·Sergi ValverdeXavier Lladó
Oct 13, 2018·Brain : a Journal of Neurology·Natalia Gonzalez CalditoPeter A Calabresi
May 20, 2020·Scientific Reports·Aaron CarassIpek Oguz
Mar 15, 2020·Annual Review of Biomedical Engineering·John A OnofreyJames S Duncan
Mar 27, 2019·Annals of Clinical and Translational Neurology·Nora E FritzKathleen M Zackowski
Dec 1, 2019·Scientific Reports·Monan Wang, Pengcheng Li
Jun 24, 2017·AJNR. American Journal of Neuroradiology·R T ShinoharaUNKNOWN NAIMS Cooperative
Nov 20, 2016·NeuroImage·Snehashis RoyUNKNOWN Alzheimers Disease Neuroimaging Initiative
Mar 28, 2021·Computers in Biology and Medicine·Morghan HartmannRuth Dobson
Jun 5, 2021·AJNR. American Journal of Neuroradiology·B E DeweyP Nyquist
Jul 28, 2021·Medical & Biological Engineering & Computing·Shiri GordonTammy Riklin Raviv
Aug 31, 2021·Frontiers in Immunology·Faezeh MoazamiVassili Soumelis

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