A Spatiotemporal Statistical Model for Eyeballs of Human Embryos
-
- KISHIMOTO Masashi
- Tokyo University of Agriculture and Technology
-
- SAITO Atsushi
- Tokyo University of Agriculture and Technology
-
- TAKAKUWA Tetsuya
- Kyoto University
-
- YAMADA Shigehito
- Kyoto University
-
- MATSUZOE Hiroshi
- Nagoya Institute of Technology
-
- HONTANI Hidekata
- Nagoya Institute of Technology
-
- SHIMIZU Akinobu
- Tokyo University of Agriculture and Technology
Abstract
<p>During the development of a human embryo, the position of eyes moves medially and caudally in the viscerocranium. A statistical model of this process can play an important role in embryology by facilitating qualitative analyses of change. This paper proposes an algorithm to construct a spatiotemporal statistical model for the eyeballs of a human embryo. The proposed modeling algorithm builds a statistical model of the spatial coordinates of the eyeballs independently for each Carnegie stage (CS) by using principal component analysis (PCA). In the process, a q-Gaussian distribution with a model selection scheme based on the Aaike information criterion is used to handle a non-Gaussian distribution with a small sample size. Subsequently, it seamlessly interpolates the statistical models of neighboring CSs, and we present 10 interpolation methods. We also propose an estimation algorithm for the CS using our spatiotemporal statistical model. A set of images of eyeballs in human embryos from the Kyoto Collection was used to train the model and assess its performance. The modeling results suggested that information geometry-based interpolation under the assumption of a q-Gaussian distribution is the best modeling method. The average error in CS estimation was 0.409. We proposed an algorithm to construct a spatiotemporal statistical model of the eyeballs of a human embryo and tested its performance using the Kyoto Collection.</p>
Journal
-
- IEICE Transactions on Information and Systems
-
IEICE Transactions on Information and Systems E100.D (7), 1505-1515, 2017
The Institute of Electronics, Information and Communication Engineers
- Tweet
Details 詳細情報について
-
- CRID
- 1390001204378098560
-
- NII Article ID
- 130006792950
-
- ISSN
- 17451361
- 09168532
-
- Text Lang
- en
-
- Data Source
-
- JaLC
- Crossref
- CiNii Articles
- KAKEN
-
- Abstract License Flag
- Disallowed