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

工学科　知能情報工学コース

研究成果: 書籍の章/レポート/会議録 › 会議への寄与 › 査読

1 被引用数 (Scopus)

抄録

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.

本文言語	英語
ホスト出版物のタイトル	Advanced Biomedical and Clinical Diagnostic and Surgical Guidance Systems XXI
編集者	Caroline Boudoux, James W. Tunnell
出版社	SPIE
ISBN（電子版）	9781510658417
DOI	https://doi.org/10.1117/12.2650175
出版ステータス	出版済み - 2023
イベント	Advanced Biomedical and Clinical Diagnostic and Surgical Guidance Systems XXI 2023 - San Francisco, 米国継続期間: 2023/01/28 → 2023/01/29

出版物シリーズ

名前	Progress in Biomedical Optics and Imaging - Proceedings of SPIE
巻	12368
ISSN（印刷版）	1605-7422

学会

学会	Advanced Biomedical and Clinical Diagnostic and Surgical Guidance Systems XXI 2023
国/地域	米国
City	San Francisco
Period	2023/01/28 → 2023/01/29

ASJC Scopus 主題領域

電子材料、光学材料、および磁性材料
原子分子物理学および光学
生体材料
放射線学、核医学およびイメージング

UN SDG

この成果は、次の持続可能な開発目標に貢献しています

文献へのアクセス

10.1117/12.2650175

フィンガープリント

「Optical biopsy technique for detection of aganglionosis in Hirschsprung disease by Raman spectroscopy combined with deep learning」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル

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 記事 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 その他. / 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",

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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, 米国, 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 その他.
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).

研究成果: 書籍の章/レポート/会議録 › 会議への寄与 › 査読

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

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

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A2 - Tunnell, James W.

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

Y2 - 28 January 2023 through 29 January 2023

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Matsumoto Y, Ogawa K, Tamura K, Yagi R, Onishi S, Ieiri S その他. 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