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Proceedings Paper

Usefulness of fine-tuning for deep learning based multi-organ regions segmentation method from non-contrast CT volumes using small training dataset
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Paper Abstract

This paper presents segmentation of multiple organ regions from non-contrast CT volume based on deep learning. Also, we report usefulness of fine-tuning using a small number of training data for multi-organ regions segmentation. In medical image analysis system, it is vital to recognize patient specific anatomical structures in medical images such as CT volumes. We have studied on a multi-organ regions segmentation method from contrast-enhanced abdominal CT volume using 3D U-Net. Since non-contrast CT volumes are also usually used in the medical field, segmentation of multi-organ regions from non-contrast CT volume is also important for the medical image analysis system. In this study, we extract multi-organ regions from non-contrast CT volume using 3D U-Net and a small number of training data. We perform fine-tuning from a pre-trained model obtained from the previous studies. The pre-trained 3D U-Net model is trained by a large number of contrast enhanced CT volumes. Then, fine-tuning is performed using a small number of non-contrast CT volumes. The experimental results showed that the fine-tuned 3D U-Net model could extract multi-organ regions from non-contrast CT volume. The proposed training scheme using fine-tuning is useful for segmenting multi-organ regions using a small number of training data.

Paper Details

Date Published: 16 March 2020
PDF: 6 pages
Proc. SPIE 11314, Medical Imaging 2020: Computer-Aided Diagnosis, 113143V (16 March 2020); doi: 10.1117/12.2551022
Show Author Affiliations
Yuichiro Hayashi, Nagoya Univ. (Japan)
Chen Shen, Nagoya Univ. (Japan)
Holger R. Roth, Nagoya Univ. (Japan)
Masahiro Oda, Nagoya Univ. (Japan)
Kazunari Misawa, Aichi Cancer Ctr. Hospital (Japan)
Masahiro Jinzaki, Keio Univ. (Japan)
Masahiro Hashimoto, Keio Univ. (Japan)
Kanako K. Kumamaru, Juntendo Univ. (Japan)
Shigeki Aoki, Juntendo Univ. (Japan)
Kensaku Mori, Nagoya Univ. (Japan)
National Institute of Informatics (Japan)


Published in SPIE Proceedings Vol. 11314:
Medical Imaging 2020: Computer-Aided Diagnosis
Horst K. Hahn; Maciej A. Mazurowski, Editor(s)

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