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Proceedings Paper

Initial study on the classification of amyotrophic diseases using texture analysis and deep learning in whole-body CT images
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Paper Abstract

The skeletal muscle exists in the whole body and can be observed in many cross sections in various tomographic images. Skeletal muscle atrophy is due to aging and disease, and the abnormality is difficult to distinguish visually. In addition, although skeletal muscle analysis requires a technique for accurate site-specific measurement of skeletal muscle, it is only realized in a limited region. We realized automatic site-specific recognition of skeletal muscle from whole-body CT images using model-based methods. Three-dimensional texture analysis revealed imaging features with statistically significant differences between amyotrophic lateral sclerosis (ALS) and other muscular diseases accompanied by atrophy. In recent years, deep learning technique is also used in the field of computer-aided diagnosis. Therefore, in this initial study, we performed automatic classification of amyotrophic diseases using deep learning for the upper extremity and lower limb regions. The classification accuracy was highest in the right forearm, which was 0.960 at the maximum (0.903 on average). In the future, methods for differentiating more kinds of muscular atrophy and clinical application of ALS detection by analyzing muscular regions must be considered.

Paper Details

Date Published: 27 March 2019
PDF: 4 pages
Proc. SPIE 11050, International Forum on Medical Imaging in Asia 2019, 110500X (27 March 2019); doi: 10.1117/12.2518199
Show Author Affiliations
N. Kamiya, Aichi Prefectural Univ. (Japan)
A. Oshima, Aichi Prefectural Univ. (Japan)
E. Asano, Gifu Univ. (Japan)
X. Zhou, Gifu Univ. (Japan)
M. Yamada, Gifu Univ. (Japan)
H. Kato, Gifu Univ. (Japan)
C. Muramatsu, Gifu Univ. (Japan)
T. Hara, Gifu Univ. (Japan)
T. Miyoshi, Gifu Univ. Hospital (Japan)
M. Matsuo, Gifu Univ. (Japan)
H. Fujita, Gifu Univ. (Japan)


Published in SPIE Proceedings Vol. 11050:
International Forum on Medical Imaging in Asia 2019
Feng Lin; Hiroshi Fujita; Jong Hyo Kim, Editor(s)

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