Prof. Masayuki Matsuo Profile

Prof. Masayuki Matsuo

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Publications (3)

Proceedings Article | 27 March 2019 Paper

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

N. Kamiya, A. Oshima, E. Asano, X. Zhou, M. Yamada, H. Kato, C. Muramatsu, T. Hara, T. Miyoshi, M. Matsuo, H. Fujita

Proceedings Volume 11050, 110500X (2019) https://doi.org/10.1117/12.2518199

KEYWORDS: Computed tomography, Statistical analysis, Image classification, Inspection, Radiology, Visualization, Computer aided diagnosis and therapy, Analytical research, Information science, Information technology

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Proceedings Article | 3 March 2017 Paper

Automated assessment of breast tissue density in non-contrast 3D CT images without image segmentation based on a deep CNN

Xiangrong Zhou, Takuya Kano, Hiromi Koyasu, Shuo Li, Xinxin Zhou, Takeshi Hara, Masayuki Matsuo, Hiroshi Fujita

Proceedings Volume 10134, 101342Q (2017) https://doi.org/10.1117/12.2254320

KEYWORDS: Breast, Computed tomography, Image segmentation, X-ray computed tomography, 3D image processing, Mammary gland, Image processing, Image classification, X-ray imaging, X-rays, Tissues, Breast cancer, Medicine, 3D acquisition

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Proceedings Article | 3 March 2017 Paper

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

Naoki Kamiya, Kosuke Ieda, Xiangrong Zhou, Megumi Yamada, Hiroki Kato, Chisako Muramatsu, Takeshi Hara, Toshiharu Miyoshi, Takashi Inuzuka, Masayuki Matsuo, Hiroshi Fujita

Proceedings Volume 10134, 1013442 (2017) https://doi.org/10.1117/12.2251584

KEYWORDS: Computed tomography, Computer aided diagnosis and therapy, Solid modeling, Diagnostics, Inspection, Image segmentation, Visualization, Computer aided design, Abdomen, Lung, 3D modeling, Medicine, Radiology

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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.

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