A Spatiotemporal Statistical Model for Eyeballs of Human Embryos

Masashi KISHIMOTO; Atsushi SAITO; Tetsuya TAKAKUWA; Shigehito YAMADA; Hiroshi MATSUZOE; Hidekata HONTANI; Akinobu SHIMIZU

doi:10.1587/transinf.2016EDP7493

A Spatiotemporal Statistical Model for Eyeballs of Human Embryos

Masashi KISHIMOTO, Atsushi SAITO, Tetsuya TAKAKUWA, Shigehito YAMADA, Hiroshi MATSUZOE, Hidekata HONTANI, Akinobu SHIMIZU

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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.

Publication: IEICE TRANSACTIONS on Information Vol.E100-D No.7 pp.1505-1515

Publication Date: 2017/07/01

Publicized: 2017/04/17

Online ISSN: 1745-1361

DOI: 10.1587/transinf.2016EDP7493

Type of Manuscript: PAPER

Category: Biological Engineering

Authors

Masashi KISHIMOTO
  Tokyo University of Agriculture and Technology
Atsushi SAITO
  Tokyo University of Agriculture and Technology
Tetsuya TAKAKUWA
  Kyoto University
Shigehito YAMADA
  Kyoto University
Hiroshi MATSUZOE
  Nagoya Institute of Technology
Hidekata HONTANI
  Nagoya Institute of Technology
Akinobu SHIMIZU
  Tokyo University of Agriculture and Technology

Keyword

computational anatomy, spatiotemporal model, embryo, Carnegie stage, growth

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Masashi KISHIMOTO, Atsushi SAITO, Tetsuya TAKAKUWA, Shigehito YAMADA, Hiroshi MATSUZOE, Hidekata HONTANI, Akinobu SHIMIZU, "A Spatiotemporal Statistical Model for Eyeballs of Human Embryos" in IEICE TRANSACTIONS on Information, vol. E100-D, no. 7, pp. 1505-1515, July 2017, doi: 10.1587/transinf.2016EDP7493.
Abstract: 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.
URL: https://globals.ieice.org/en_transactions/information/10.1587/transinf.2016EDP7493/_p

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@ARTICLE{e100-d_7_1505,
author={Masashi KISHIMOTO, Atsushi SAITO, Tetsuya TAKAKUWA, Shigehito YAMADA, Hiroshi MATSUZOE, Hidekata HONTANI, Akinobu SHIMIZU, },
journal={IEICE TRANSACTIONS on Information},
title={A Spatiotemporal Statistical Model for Eyeballs of Human Embryos},
year={2017},
volume={E100-D},
number={7},
pages={1505-1515},
abstract={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.},
keywords={},
doi={10.1587/transinf.2016EDP7493},
ISSN={1745-1361},
month={July},}

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TY - JOUR
TI - A Spatiotemporal Statistical Model for Eyeballs of Human Embryos
T2 - IEICE TRANSACTIONS on Information
SP - 1505
EP - 1515
AU - Masashi KISHIMOTO
AU - Atsushi SAITO
AU - Tetsuya TAKAKUWA
AU - Shigehito YAMADA
AU - Hiroshi MATSUZOE
AU - Hidekata HONTANI
AU - Akinobu SHIMIZU
PY - 2017
DO - 10.1587/transinf.2016EDP7493
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E100-D
IS - 7
JA - IEICE TRANSACTIONS on Information
Y1 - July 2017
AB - 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.
ER -