Paper Info
Title
Automated analysis of whole skeletal muscle for muscular atrophy detection of ALS in whole-body CT images: preliminary study.
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.
Year
DOI
Venue
2017
10.1117/12.2251584
Proceedings of SPIE
Keywords
Field
DocType
Amyotrophic lateral sclerosis,Muscular atrophy,Computer-aided diagnosis,Body cavity
Computer vision,Anatomy,Abdominal cavity,Abdomen,Skeletal muscle,Amyotrophic lateral sclerosis,Muscle atrophy,Artificial intelligence,Body cavity,Thoracic cavity,Atrophy,Physics
Conference
Volume
ISSN
Citations 
10134
0277-786X
0
PageRank 
References 
Authors
0.34
1
11
Name
Order
Citations
PageRank
N Kamiya142.56
Kosuke Ieda200.34
Xiangrong Zhou332545.53
Megumi Yamada400.34
Hiroki Kato521.35
Chisako Muramatsu631735.56
Takeshi Hara763979.10
Toshiharu Miyoshi800.68
Takashi Inuzuka900.34
Masayuki Matsuo1000.68
Hiroshi Fujita1111824.65