@inproceedings{70efb206d5764655922fbe41c6d98abf,
title = "Usefulness of fine-tuning for deep learning based multi-organ regions segmentation method from non-contrast CT volumes using small training dataset",
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.",
keywords = "ct, deep learning, ne-tuning, segmentaion",
author = "Yuichiro Hayashi and Chen Shen and Roth, {Holger R.} and Masahiro Oda and Kazunari Misawa and Masahiro Jinzaki and Masahiro Hashimoto and Kumamaru, {Kanako K.} and Shigeki Aoki and Kensaku Mori",
note = "Funding Information: The authors thank our colleagues for their suggestions and advice. This work was supported in part by the Practical Research for Innovative Cancer Control from the Japan Agency for Medical Research and Development, Funding Information: AMED, under Grant Numbers JP18lk1010028s0401 and JP19lk1010036h0001, and by a Japan Society for the Promotion of Science, JSPS, KAKENHI Grant Numbers, JP26108006 and JP17H00867. Publisher Copyright: {\textcopyright} 2020 SPIE.; Medical Imaging 2020: Computer-Aided Diagnosis ; Conference date: 16-02-2020 Through 19-02-2020",
year = "2020",
doi = "10.1117/12.2551022",
language = "English",
series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",
publisher = "SPIE",
editor = "Hahn, {Horst K.} and Mazurowski, {Maciej A.}",
booktitle = "Medical Imaging 2020",
}