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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
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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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