CA2 area detection from hippocampal microscope images using deep learning

Shohei Morinaga; Tomoe Ishikawa; Masato Yasui; Mototsugu Hamada; Tadahiro Kuroda

doi:10.1109/MWSCAS47672.2021.9531735

CA2 area detection from hippocampal microscope images using deep learning

Shohei Morinaga, Tomoe Ishikawa, Masato Yasui, Mototsugu Hamada, Tadahiro Kuroda

Department of Pharmacology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

In this study, we created a semantic segmentation model to recognize the Cornu Ammonis 2 (CA2) area inside the horizontal hippocampal slice section in a microscopic transparent-light image, which was only recognizable by biomarkers such as immunohistochemistry. The U-Net was modified so that we could incorporate the way how experts recognized the CA2 area. We achieved 100% accuracy and 84% precision. We built a system on an edge computing device and provided a practical microscope solution to assist neuroscientists.

Original language	English
Title of host publication	2021 IEEE International Midwest Symposium on Circuits and Systems, MWSCAS 2021 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	603-606
Number of pages	4
ISBN (Electronic)	9781665424615
DOIs	https://doi.org/10.1109/MWSCAS47672.2021.9531735
Publication status	Published - 2021 Aug 9
Event	2021 IEEE International Midwest Symposium on Circuits and Systems, MWSCAS 2021 - Virtual, East Lansing, United States Duration: 2021 Aug 9 → 2021 Aug 11

Publication series

Name	Midwest Symposium on Circuits and Systems
Volume	2021-August
ISSN (Print)	1548-3746

Conference

Conference	2021 IEEE International Midwest Symposium on Circuits and Systems, MWSCAS 2021
Country/Territory	United States
City	Virtual, East Lansing
Period	21/8/9 → 21/8/11

Keywords

convolutional neural networks
deep learning
microscope image
semantic segmentation

ASJC Scopus subject areas

Electronic, Optical and Magnetic Materials
Electrical and Electronic Engineering

Access to Document

10.1109/MWSCAS47672.2021.9531735

Cite this

Morinaga, S., Ishikawa, T., Yasui, M., Hamada, M., & Kuroda, T. (2021). CA2 area detection from hippocampal microscope images using deep learning. In 2021 IEEE International Midwest Symposium on Circuits and Systems, MWSCAS 2021 - Proceedings (pp. 603-606). (Midwest Symposium on Circuits and Systems; Vol. 2021-August). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/MWSCAS47672.2021.9531735

CA2 area detection from hippocampal microscope images using deep learning. / Morinaga, Shohei; Ishikawa, Tomoe; Yasui, Masato et al.
2021 IEEE International Midwest Symposium on Circuits and Systems, MWSCAS 2021 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2021. p. 603-606 (Midwest Symposium on Circuits and Systems; Vol. 2021-August).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Morinaga, S, Ishikawa, T, Yasui, M, Hamada, M & Kuroda, T 2021, CA2 area detection from hippocampal microscope images using deep learning. in 2021 IEEE International Midwest Symposium on Circuits and Systems, MWSCAS 2021 - Proceedings. Midwest Symposium on Circuits and Systems, vol. 2021-August, Institute of Electrical and Electronics Engineers Inc., pp. 603-606, 2021 IEEE International Midwest Symposium on Circuits and Systems, MWSCAS 2021, Virtual, East Lansing, United States, 21/8/9. https://doi.org/10.1109/MWSCAS47672.2021.9531735

Morinaga S, Ishikawa T, Yasui M, Hamada M, Kuroda T. CA2 area detection from hippocampal microscope images using deep learning. In 2021 IEEE International Midwest Symposium on Circuits and Systems, MWSCAS 2021 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2021. p. 603-606. (Midwest Symposium on Circuits and Systems). doi: 10.1109/MWSCAS47672.2021.9531735

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abstract = "In this study, we created a semantic segmentation model to recognize the Cornu Ammonis 2 (CA2) area inside the horizontal hippocampal slice section in a microscopic transparent-light image, which was only recognizable by biomarkers such as immunohistochemistry. The U-Net was modified so that we could incorporate the way how experts recognized the CA2 area. We achieved 100% accuracy and 84% precision. We built a system on an edge computing device and provided a practical microscope solution to assist neuroscientists. ",

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CA2 area detection from hippocampal microscope images using deep learning

Abstract

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Keywords

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