Journal article
Learning-Based Cost Functions for 3-D and 4-D Multi-Surface Multi-Object Segmentation of Knee MRI: Data From the Osteoarthritis Initiative
IEEE transactions on medical imaging, Vol.37(5), pp.1103-1113
05/2018
DOI: 10.1109/TMI.2017.2781541
PMCID: PMC5995124
PMID: 29727274
Abstract
A fully automated knee magnetic resonance imaging (MRI) segmentation method to study osteoarthritis (OA) was developed using a novel hierarchical set of random forests (RF) classifiers to learn the appearance of cartilage regions and their boundaries. A neighborhood approximation forest is used first to provide contextual feature to the second-level RF classifier that also considers local features and produces location-specific costs for the layered optimal graph image segmentation of multiple objects and surfaces (LOGISMOS) framework. Double-echo steady state MRIs used in this paper originated from the OA Initiative study. Trained on 34 MRIs with varying degrees of OA, the performance of the learning-based method tested on 108 MRIs showed significant reduction in segmentation errors (p <; 0.05) compared with the conventional gradient-based and single-stage RF-learned costs. The 3-D LOGISMOS was extended to longitudinal-3-D (4-D) to simultaneously segment multiple follow-up visits of the same patient. As such, data from all time-points of the temporal sequence contribute information to a single optimal solution that utilizes both spatial 3-D and temporal contexts. 4-D LOGISMOS validation on 108 MRIs from baseline, and 12 month follow-up scans of 54 patients showed significant reduction in segmentation errors (p <; 0.01) compared with 3-D. Finally, the potential of 4-D LOGISMOS was further explored on the same 54 patients using five annual follow-up scans demonstrating a significant improvement of measuring cartilage thickness (p <; 0.01) compared with the sequential 3-D approach.
Details
- Title: Subtitle
- Learning-Based Cost Functions for 3-D and 4-D Multi-Surface Multi-Object Segmentation of Knee MRI: Data From the Osteoarthritis Initiative
- Creators
- Satyananda Kashyap - University of IowaHonghai Zhang - University of IowaKaran Rao - University of IowaMilan Sonka - University of Iowa
- Resource Type
- Journal article
- Publication Details
- IEEE transactions on medical imaging, Vol.37(5), pp.1103-1113
- DOI
- 10.1109/TMI.2017.2781541
- PMID
- 29727274
- PMCID
- PMC5995124
- NLM abbreviation
- IEEE Trans Med Imaging
- ISSN
- 0278-0062
- eISSN
- 1558-254X
- Publisher
- Institute of Electrical and Electronics Engineers
- Grant note
- N01-AR-2-2258; N01-AR-2-2259; N01-AR-2-2260; N01-AR-2-2261; N01-AR-2-2262 / OAI is a public-private partnership comprised of five contracts funded by the National Institutes of Health, a branch of the Department of Health and Human Services, and conducted by the OAI Study Investigators (10.13039/100000002) No-R01 EB004640 / NIH (10.13039/100000070) Merck Research Laboratories; Novartis Pharmaceuticals Corporation, GlaxoSmithKline; and Pfizer, Inc. Private sector funding for the OAI is managed by the Foundation for the National Institutes of Health (10.13039/100000002)
- Language
- English
- Date published
- 05/2018
- Academic Unit
- Roy J. Carver Department of Biomedical Engineering; Electrical and Computer Engineering; Radiation Oncology; The Iowa Institute for Biomedical Imaging; Fraternal Order of Eagles Diabetes Research Center; Injury Prevention Research Center; Ophthalmology and Visual Sciences
- Record Identifier
- 9984186696602771
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