Spatiotemporal Statistical Model of Anatomical Landmarks on a Human Embryonic Brain

Aoi Shinjo, Atsushi Saito, Tetsuya Takakuwa, Shigehito Yamada, Hidekata Hontani, Hiroshi Matsuzoe, Shoko Miyauchi, Kenichi Morooka, Akinobu Shimizu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We propose a new method for constructing a spatiotemporal statistical model of the distribution of anatomical landmarks (LMs) of a human embryo. This method exhibits potential for the quantitative assessment of the extent of anomalies and is important in the research of congenital malformations. However, a few of the LMs might not be observed at a specific developmental stage because large morphological deformations exist during the early stages of development. It is difficult for conventional statistical shape analysis methods to handle missing LMs in the training dataset. The basic concept of the proposed method is to conduct statistical analyses by predicting and completing the coordinates of the missing LMs. We demonstrated the proposed method in the context of spatiotemporal statistical modeling of 10 LMs on the brain surface using 37 embryonic subjects with Carnegie stages of 19–22. We conducted a comparative study of the spatiotemporal statistical models between four different prediction methods, and we found that deformable surface mapping was the best prediction method in terms of model generalization and specificity.

Original languageEnglish
Title of host publicationUncertainty for Safe Utilization of Machine Learning in Medical Imaging and Clinical Image-Based Procedures - 1st International Workshop, UNSURE 2019, and 8th International Workshop, CLIP 2019, Held in Conjunction with MICCAI 2019, Proceedings
EditorsHayit Greenspan, Ryutaro Tanno, Marius Erdt, Tal Arbel, Christian Baumgartner, Adrian Dalca, Carole H. Sudre, William M. Wells, Klaus Drechsler, Marius Erdt, Marius George Linguraru, Raj Shekhar, Cristina Oyarzun Laura, Stefan Wesarg, Miguel Ángel González Ballester
PublisherSpringer
Pages94-103
Number of pages10
ISBN (Print)9783030326883
DOIs
Publication statusPublished - Jan 1 2019
Event1st International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, UNSURE 2019, and the 8th International Workshop on Clinical Image-Based Procedures, CLIP 2019, held in conjunction with 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019 - Shenzhen, China
Duration: Oct 17 2019Oct 17 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11840 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference1st International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, UNSURE 2019, and the 8th International Workshop on Clinical Image-Based Procedures, CLIP 2019, held in conjunction with 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019
CountryChina
CityShenzhen
Period10/17/1910/17/19

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Shinjo, A., Saito, A., Takakuwa, T., Yamada, S., Hontani, H., Matsuzoe, H., Miyauchi, S., Morooka, K., & Shimizu, A. (2019). Spatiotemporal Statistical Model of Anatomical Landmarks on a Human Embryonic Brain. In H. Greenspan, R. Tanno, M. Erdt, T. Arbel, C. Baumgartner, A. Dalca, C. H. Sudre, W. M. Wells, K. Drechsler, M. Erdt, M. G. Linguraru, R. Shekhar, C. Oyarzun Laura, S. Wesarg, & M. Á. González Ballester (Eds.), Uncertainty for Safe Utilization of Machine Learning in Medical Imaging and Clinical Image-Based Procedures - 1st International Workshop, UNSURE 2019, and 8th International Workshop, CLIP 2019, Held in Conjunction with MICCAI 2019, Proceedings (pp. 94-103). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11840 LNCS). Springer. https://doi.org/10.1007/978-3-030-32689-0_10