Paper Abstract and Keywords |
Presentation |
2014-01-26 13:30
Newborn brain growth model using manifold learning Ryosuke Nakano (Univ. of Hyogo), Syoji Kobashi, Kei Kuramoto (Univ. of Hyogo/WPI-IFReC), Yuki Wakata, Kumiko Ando, Reiichi Ishikura (Hyogo College of Medicine), Tomomoto Ishikawa (Ishikawa Hospital), Shozo Hirota (Hyogo College of Medicine), Yutaka Hata (Univ. of Hyogo/Osaka Univ.) MI2013-64 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
To develop a computer-aided diagnosis system for neonatal cerebral disorders, some literatures have shown atlas-based methods for segmenting parenchymal region in MR images. Because neonatal cerebrum deforms quickly by natural growth, we desire an atlas growth model. This paper proposes two methods for generating fuzzy object growth model (FOGM), which is an extension of fuzzy object model (FOM). The first method generates a growth-index weighted FOM in which the index is calculated from age. Because the growth index will be different from person to person even though the same age, the second method estimates the growth-index from MR images using manifold learning. To evaluate the proposed methods, we segment the parenchymal region of 16 neonatal subjects (revised age; 0-2 years old). The results showed that FOGM was superior to FOM, and manifold learning based method gave the best accuracy. And, the growth index estimated with manifold learning was significantly correlated with both of age and cerebral volume (p<0.001). |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
Newborn Brain / Manifold Learning / Fuzzy Object Model / Fuzzy Object Growth Model / MR Image / / / |
Reference Info. |
IEICE Tech. Rep., vol. 113, no. 410, MI2013-64, pp. 47-52, Jan. 2014. |
Paper # |
MI2013-64 |
Date of Issue |
2014-01-19 (MI) |
ISSN |
Print edition: ISSN 0913-5685 Online edition: ISSN 2432-6380 |
Copyright and reproduction |
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MI2013-64 |
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