Optical biopsy technique for detection of aganglionosis in Hirschsprung disease by Raman spectroscopy combined with deep learning

Yuki Matsumoto; Katsuhiro Ogawa; Kai Tamura; Rena Yagi; Shun Onishi; Satoshi Ieiri; Tsuyoshi Etoh; Masafumi Inomata; Takashi Katagiri; Yusuke Oshima

doi:10.1117/12.2650175

Optical biopsy technique for detection of aganglionosis in Hirschsprung disease by Raman spectroscopy combined with deep learning

Yuki Matsumoto, Katsuhiro Ogawa, Kai Tamura, Rena Yagi, Shun Onishi, Satoshi Ieiri, Tsuyoshi Etoh, Masafumi Inomata, Takashi Katagiri, Yusuke Oshima

Intellectual Information Engineering

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

1 Scopus citations

Abstract

In this study, we aimed to develop a new optical biopsy technique for aganglionosis of Hirschsprung disease (HSCR) and we then evaluated a custom designed Raman optical biopsy system combined with deep learning based on convolutional neural networks (CNNs). Surgical specimens of formalin-fixed tissue of HSCR patients were subjected to this study. In the result, we achieved more than 90% classification accuracy between the normal and the lesion segments in mucosa. This study shows that CNN is useful for discriminating Raman spectra of the human gastrointestinal wall.

Original language	English
Title of host publication	Advanced Biomedical and Clinical Diagnostic and Surgical Guidance Systems XXI
Editors	Caroline Boudoux, James W. Tunnell
Publisher	SPIE
ISBN (Electronic)	9781510658417
DOIs	https://doi.org/10.1117/12.2650175
State	Published - 2023
Event	Advanced Biomedical and Clinical Diagnostic and Surgical Guidance Systems XXI 2023 - San Francisco, United States Duration: 2023/01/28 → 2023/01/29

Publication series

Name	Progress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume	12368
ISSN (Print)	1605-7422

Conference

Conference	Advanced Biomedical and Clinical Diagnostic and Surgical Guidance Systems XXI 2023
Country/Territory	United States
City	San Francisco
Period	2023/01/28 → 2023/01/29

Keywords

Enteric nervous system
Hirschsprung's disease
Machine learning
Raman spectroscopy

ASJC Scopus subject areas

Electronic, Optical and Magnetic Materials
Atomic and Molecular Physics, and Optics
Biomaterials
Radiology Nuclear Medicine and imaging

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1117/12.2650175

Cite this

Matsumoto, Y., Ogawa, K., Tamura, K., Yagi, R., Onishi, S., Ieiri, S., Etoh, T., Inomata, M., Katagiri, T., & Oshima, Y. (2023). Optical biopsy technique for detection of aganglionosis in Hirschsprung disease by Raman spectroscopy combined with deep learning. In C. Boudoux, & J. W. Tunnell (Eds.), Advanced Biomedical and Clinical Diagnostic and Surgical Guidance Systems XXI Article 123680B (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 12368). SPIE. https://doi.org/10.1117/12.2650175

Matsumoto, Yuki ; Ogawa, Katsuhiro ; Tamura, Kai et al. / Optical biopsy technique for detection of aganglionosis in Hirschsprung disease by Raman spectroscopy combined with deep learning. Advanced Biomedical and Clinical Diagnostic and Surgical Guidance Systems XXI. editor / Caroline Boudoux ; James W. Tunnell. SPIE, 2023. (Progress in Biomedical Optics and Imaging - Proceedings of SPIE).

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abstract = "In this study, we aimed to develop a new optical biopsy technique for aganglionosis of Hirschsprung disease (HSCR) and we then evaluated a custom designed Raman optical biopsy system combined with deep learning based on convolutional neural networks (CNNs). Surgical specimens of formalin-fixed tissue of HSCR patients were subjected to this study. In the result, we achieved more than 90% classification accuracy between the normal and the lesion segments in mucosa. This study shows that CNN is useful for discriminating Raman spectra of the human gastrointestinal wall.",

keywords = "Enteric nervous system, Hirschsprung's disease, Machine learning, Raman spectroscopy",

author = "Yuki Matsumoto and Katsuhiro Ogawa and Kai Tamura and Rena Yagi and Shun Onishi and Satoshi Ieiri and Tsuyoshi Etoh and Masafumi Inomata and Takashi Katagiri and Yusuke Oshima",

note = "Publisher Copyright: {\textcopyright} COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.; Advanced Biomedical and Clinical Diagnostic and Surgical Guidance Systems XXI 2023 ; Conference date: 28-01-2023 Through 29-01-2023",

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doi = "10.1117/12.2650175",

language = "英語",

series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",

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Matsumoto, Y, Ogawa, K, Tamura, K, Yagi, R, Onishi, S, Ieiri, S, Etoh, T, Inomata, M, Katagiri, T & Oshima, Y 2023, Optical biopsy technique for detection of aganglionosis in Hirschsprung disease by Raman spectroscopy combined with deep learning. in C Boudoux & JW Tunnell (eds), Advanced Biomedical and Clinical Diagnostic and Surgical Guidance Systems XXI., 123680B, Progress in Biomedical Optics and Imaging - Proceedings of SPIE, vol. 12368, SPIE, Advanced Biomedical and Clinical Diagnostic and Surgical Guidance Systems XXI 2023, San Francisco, United States, 2023/01/28. https://doi.org/10.1117/12.2650175

Optical biopsy technique for detection of aganglionosis in Hirschsprung disease by Raman spectroscopy combined with deep learning. / Matsumoto, Yuki; Ogawa, Katsuhiro; Tamura, Kai et al.
Advanced Biomedical and Clinical Diagnostic and Surgical Guidance Systems XXI. ed. / Caroline Boudoux; James W. Tunnell. SPIE, 2023. 123680B (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 12368).

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

TY - GEN

T1 - Optical biopsy technique for detection of aganglionosis in Hirschsprung disease by Raman spectroscopy combined with deep learning

AU - Matsumoto, Yuki

AU - Ogawa, Katsuhiro

AU - Tamura, Kai

AU - Yagi, Rena

AU - Onishi, Shun

AU - Ieiri, Satoshi

AU - Etoh, Tsuyoshi

AU - Inomata, Masafumi

AU - Katagiri, Takashi

AU - Oshima, Yusuke

PY - 2023

Y1 - 2023

N2 - In this study, we aimed to develop a new optical biopsy technique for aganglionosis of Hirschsprung disease (HSCR) and we then evaluated a custom designed Raman optical biopsy system combined with deep learning based on convolutional neural networks (CNNs). Surgical specimens of formalin-fixed tissue of HSCR patients were subjected to this study. In the result, we achieved more than 90% classification accuracy between the normal and the lesion segments in mucosa. This study shows that CNN is useful for discriminating Raman spectra of the human gastrointestinal wall.

AB - In this study, we aimed to develop a new optical biopsy technique for aganglionosis of Hirschsprung disease (HSCR) and we then evaluated a custom designed Raman optical biopsy system combined with deep learning based on convolutional neural networks (CNNs). Surgical specimens of formalin-fixed tissue of HSCR patients were subjected to this study. In the result, we achieved more than 90% classification accuracy between the normal and the lesion segments in mucosa. This study shows that CNN is useful for discriminating Raman spectra of the human gastrointestinal wall.

KW - Enteric nervous system

KW - Hirschsprung's disease

KW - Machine learning

KW - Raman spectroscopy

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M3 - 会議への寄与

AN - SCOPUS:85152773657

T3 - Progress in Biomedical Optics and Imaging - Proceedings of SPIE

BT - Advanced Biomedical and Clinical Diagnostic and Surgical Guidance Systems XXI

A2 - Boudoux, Caroline

A2 - Tunnell, James W.

PB - SPIE

T2 - Advanced Biomedical and Clinical Diagnostic and Surgical Guidance Systems XXI 2023

Y2 - 28 January 2023 through 29 January 2023

ER -

Matsumoto Y, Ogawa K, Tamura K, Yagi R, Onishi S, Ieiri S et al. Optical biopsy technique for detection of aganglionosis in Hirschsprung disease by Raman spectroscopy combined with deep learning. In Boudoux C, Tunnell JW, editors, Advanced Biomedical and Clinical Diagnostic and Surgical Guidance Systems XXI. SPIE. 2023. 123680B. (Progress in Biomedical Optics and Imaging - Proceedings of SPIE). doi: 10.1117/12.2650175

Optical biopsy technique for detection of aganglionosis in Hirschsprung disease by Raman spectroscopy combined with deep learning

Abstract

Publication series

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Keywords

ASJC Scopus subject areas

UN SDGs

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