Conference proceeding
Tensor-Based Grading: A Novel Patch-Based Grading Approach for the Analysis Of Deformation Fields in Huntington's Disease
2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), Vol.2020-, pp.1091-1095
04/2020
DOI: 10.1109/ISBI45749.2020.9098692
PMCID: PMC8643362
PMID: 34873434
Abstract
The improvements in magnetic resonance imaging have led to the development of numerous techniques to better detect structural alterations caused by neurodegenerative diseases. Among these, the patch-based grading framework has been proposed to model local patterns of anatomical changes. This approach is attractive because of its low computational cost and its competitive performance. Other studies have proposed to analyze the deformations of brain structures using tensor-based morphometry, which is a highly interpretable approach. In this work, we propose to combine the advantages of these two approaches by extending the patch-based grading framework with a new tensor-based grading method that enables us to model patterns of local deformation using a log-Euclidean metric. We evaluate our new method in a study of the putamen for the classification of patients with pre-manifest Huntington's disease and healthy controls. Our experiments show a substantial increase in classification accuracy (87.5 ± 0.5 vs. 81.3 ± 0.6) compared to the existing patch-based grading methods, and a good complement to putamen volume, which is a primary imaging-based marker for the study of Hunting-ton's disease.
Details
- Title: Subtitle
- Tensor-Based Grading: A Novel Patch-Based Grading Approach for the Analysis Of Deformation Fields in Huntington's Disease
- Creators
- Kilian Hett - Vanderbilt UniversityHans Johnson - University of IowaPierrick Coupe - Université de BordeauxJane S Paulsen - University of IowaJeffrey D Long - University of IowaIpek Oguz - Vanderbilt University
- Resource Type
- Conference proceeding
- Publication Details
- 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), Vol.2020-, pp.1091-1095
- DOI
- 10.1109/ISBI45749.2020.9098692
- PMID
- 34873434
- PMCID
- PMC8643362
- NLM abbreviation
- Proc IEEE Int Symp Biomed Imaging
- ISSN
- 1945-7928
- eISSN
- 1945-8452
- Publisher
- IEEE
- Language
- English
- Date published
- 04/2020
- Academic Unit
- Roy J. Carver Department of Biomedical Engineering; Electrical and Computer Engineering; Psychiatry; Psychological and Brain Sciences; Biostatistics; The Iowa Institute for Biomedical Imaging; The Iowa Initiative for Artificial Intelligence; Iowa Informatics Initiative
- Record Identifier
- 9984280875902771
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