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

Yuichiro Hayashi; Chen Shen; Holger R. Roth; Masahiro Oda; Kazunari Misawa; Masahiro Jinzaki; Masahiro Hashimoto; Kanako K. Kumamaru; Shigeki Aoki; Kensaku Mori

doi:10.1117/12.2551022

16 March 2020 Usefulness of fine-tuning for deep learning based multi-organ regions segmentation method from non-contrast CT volumes using small training dataset

Yuichiro Hayashi, Chen Shen, Holger R. Roth, Masahiro Oda, Kazunari Misawa, Masahiro Jinzaki, Masahiro Hashimoto, Kanako K. Kumamaru, Shigeki Aoki, Kensaku Mori

Author Affiliations +

Proceedings Volume 11314, Medical Imaging 2020: Computer-Aided Diagnosis; 113143V (2020) https://doi.org/10.1117/12.2551022
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

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.

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Yuichiro Hayashi, Chen Shen, Holger R. Roth, Masahiro Oda, Kazunari Misawa, Masahiro Jinzaki, Masahiro Hashimoto, Kanako K. Kumamaru, Shigeki Aoki, and Kensaku Mori "Usefulness of fine-tuning for deep learning based multi-organ regions segmentation method from non-contrast CT volumes using small training dataset", Proc. SPIE 11314, Medical Imaging 2020: Computer-Aided Diagnosis, 113143V (16 March 2020); https://doi.org/10.1117/12.2551022

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KEYWORDS

3D modeling

Image segmentation

Computed tomography

Stomach

Data modeling

Arteries

Medical imaging

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