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Proceedings Paper

Automated analysis of whole skeletal muscle for muscular atrophy detection of ALS in whole-body CT images: preliminary study
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Paper Abstract

Amyotrophic lateral sclerosis (ALS) causes functional disorders such as difficulty in breathing and swallowing through the atrophy of voluntary muscles. ALS in its early stages is difficult to diagnose because of the difficulty in differentiating it from other muscular diseases. In addition, image inspection methods for aggressive diagnosis for ALS have not yet been established. The purpose of this study is to develop an automatic analysis system of the whole skeletal muscle to support the early differential diagnosis of ALS using whole-body CT images. In this study, the muscular atrophy parts including ALS patients are automatically identified by recognizing and segmenting whole skeletal muscle in the preliminary steps. First, the skeleton is identified by its gray value information. Second, the initial area of the body cavity is recognized by the deformation of the thoracic cavity based on the anatomical segmented skeleton. Third, the abdominal cavity boundary is recognized using ABM for precisely recognizing the body cavity. The body cavity is precisely recognized by non-rigid registration method based on the reference points of the abdominal cavity boundary. Fourth, the whole skeletal muscle is recognized by excluding the skeleton, the body cavity, and the subcutaneous fat. Additionally, the areas of muscular atrophy including ALS patients are automatically identified by comparison of the muscle mass. The experiments were carried out for ten cases with abnormality in the skeletal muscle. Global recognition and segmentation of the whole skeletal muscle were well realized in eight cases. Moreover, the areas of muscular atrophy including ALS patients were well identified in the lower limbs. As a result, this study indicated the basic technology to detect the muscle atrophy including ALS. In the future, it will be necessary to consider methods to differentiate other kinds of muscular atrophy as well as the clinical application of this detection method for early ALS detection and examine a large number of cases with stage and disease type.

Paper Details

Date Published: 3 March 2017
PDF: 6 pages
Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 1013442 (3 March 2017); doi: 10.1117/12.2251584
Show Author Affiliations
Naoki Kamiya, Aichi Prefectural Univ. (Japan)
Kosuke Ieda, Graduate School of Medicine, Gifu Univ. (Japan)
Xiangrong Zhou, Graduate School of Medicine, Gifu Univ. (Japan)
Megumi Yamada, Graduate School of Medicine, Gifu Univ. (Japan)
Hiroki Kato, Gifu Univ. (Japan)
Chisako Muramatsu, Graduate School of Medicine, Gifu Univ. (Japan)
Takeshi Hara, Graduate School of Medicine, Gifu Univ. (Japan)
Toshiharu Miyoshi, Gifu Univ. Hospital (Japan)
Takashi Inuzuka, Graduate School of Medicine, Gifu Univ. (Japan)
Masayuki Matsuo, Graduate School of Medicine, Gifu Univ. (Japan)
Hiroshi Fujita, Graduate School of Medicine, Gifu Univ. (Japan)


Published in SPIE Proceedings Vol. 10134:
Medical Imaging 2017: Computer-Aided Diagnosis
Samuel G. Armato III; Nicholas A. Petrick, Editor(s)

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