Cancers (Mar 2021)
Fine-Tuning Approach for Segmentation of Gliomas in Brain Magnetic Resonance Images with a Machine Learning Method to Normalize Image Differences among Facilities
- Satoshi Takahashi,
- Masamichi Takahashi,
- Manabu Kinoshita,
- Mototaka Miyake,
- Risa Kawaguchi,
- Naoki Shinojima,
- Akitake Mukasa,
- Kuniaki Saito,
- Motoo Nagane,
- Ryohei Otani,
- Fumi Higuchi,
- Shota Tanaka,
- Nobuhiro Hata,
- Kaoru Tamura,
- Kensuke Tateishi,
- Ryo Nishikawa,
- Hideyuki Arita,
- Masahiro Nonaka,
- Takehiro Uda,
- Junya Fukai,
- Yoshiko Okita,
- Naohiro Tsuyuguchi,
- Yonehiro Kanemura,
- Kazuma Kobayashi,
- Jun Sese,
- Koichi Ichimura,
- Yoshitaka Narita,
- Ryuji Hamamoto
Affiliations
- Satoshi Takahashi
- Division of Molecular Modification and Cancer Biology, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan
- Masamichi Takahashi
- Department of Neurosurgery and Neuro-Oncology, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan
- Manabu Kinoshita
- Department of Neurosurgery, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, Osaka 565-0871, Japan
- Mototaka Miyake
- Department of Diagnostic Radiology, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan
- Risa Kawaguchi
- Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology, 2-3-26, Aomi, Koto-ku, Tokyo 135-0064, Japan
- Naoki Shinojima
- Department of Neurosurgery, Graduate School of Medical Sciences, Kumamoto University, 1-1-1 Honjo, Chuo-ku, Kumamoto 860-8556, Japan
- Akitake Mukasa
- Department of Neurosurgery, Graduate School of Medical Sciences, Kumamoto University, 1-1-1 Honjo, Chuo-ku, Kumamoto 860-8556, Japan
- Kuniaki Saito
- Department of Neurosurgery, Kyorin University Faculty of Medicine, 6-20-2, Sinkawa, Mitaka, Tokyo 181-8611, Japan
- Motoo Nagane
- Department of Neurosurgery, Kyorin University Faculty of Medicine, 6-20-2, Sinkawa, Mitaka, Tokyo 181-8611, Japan
- Ryohei Otani
- Department of Neurosurgery, Dokkyo Medical University, 880 Kitakobayashi, Mibu, Shimotsugagun, Tochigi 321-0293, Japan
- Fumi Higuchi
- Department of Neurosurgery, Dokkyo Medical University, 880 Kitakobayashi, Mibu, Shimotsugagun, Tochigi 321-0293, Japan
- Shota Tanaka
- Department of Neurosurgery, Faculty of Medicine, The University of Tokyo, 7-3-1 Hongo Bunkyo-ku, Tokyo 113-8655, Japan
- Nobuhiro Hata
- Department of Neurosurgery, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka 812-8582, Japan
- Kaoru Tamura
- Department of Neurosurgery, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan
- Kensuke Tateishi
- Department of Neurosurgery, Graduate School of Medicine, Yokohama City University, 3-9 Fukuura, Kanazawa-ku, Yokohama, Kanagawa 236-0004, Japan
- Ryo Nishikawa
- Department of Neuro-Oncology/Neurosurgery, Saitama Medical University International Medical Center, 397-1 Yamane, Hidaka, Saitama 350-1298, Japan
- Hideyuki Arita
- Department of Neurosurgery, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, Osaka 565-0871, Japan
- Masahiro Nonaka
- Department of Neurosurgery, Kansai Medical University, 2-5-1 Shinmachi, Hirakata, Osaka 573-1010, Japan
- Takehiro Uda
- Department of Neurosurgery, Osaka City University Graduate School of Medicine, 1-4-3 Asahi-machi, Abeno-ku, Osaka 545-8585, Japan
- Junya Fukai
- Department of Neurological Surgery, Wakayama Medical University School of Medicine Wakayama, 811-1 Kimiidera, Wakayama 641-8509, Japan
- Yoshiko Okita
- Department of Neurosurgery, National Hospital Organization Osaka National Hospital, 2-1-14 Hoenzaka, Chuo-ku, Osaka 540-0006, Japan
- Naohiro Tsuyuguchi
- Department of Neurosurgery, Osaka City University Graduate School of Medicine, 1-4-3 Asahi-machi, Abeno-ku, Osaka 545-8585, Japan
- Yonehiro Kanemura
- Department of Neurosurgery, National Hospital Organization Osaka National Hospital, 2-1-14 Hoenzaka, Chuo-ku, Osaka 540-0006, Japan
- Kazuma Kobayashi
- Division of Molecular Modification and Cancer Biology, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan
- Jun Sese
- Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology, 2-3-26, Aomi, Koto-ku, Tokyo 135-0064, Japan
- Koichi Ichimura
- Division of Brain Tumor Translational Research, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan
- Yoshitaka Narita
- Department of Neurosurgery and Neuro-Oncology, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan
- Ryuji Hamamoto
- Division of Molecular Modification and Cancer Biology, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan
- DOI
- https://doi.org/10.3390/cancers13061415
- Journal volume & issue
-
Vol. 13,
no. 6
p. 1415
Abstract
Machine learning models for automated magnetic resonance image segmentation may be useful in aiding glioma detection. However, the image differences among facilities cause performance degradation and impede detection. This study proposes a method to solve this issue. We used the data from the Multimodal Brain Tumor Image Segmentation Benchmark (BraTS) and the Japanese cohort (JC) datasets. Three models for tumor segmentation are developed. In our methodology, the BraTS and JC models are trained on the BraTS and JC datasets, respectively, whereas the fine-tuning models are developed from the BraTS model and fine-tuned using the JC dataset. Our results show that the Dice coefficient score of the JC model for the test portion of the JC dataset was 0.779 ± 0.137, whereas that of the BraTS model was lower (0.717 ± 0.207). The mean Dice coefficient score of the fine-tuning model was 0.769 ± 0.138. There was a significant difference between the BraTS and JC models (p p = 0.002); however, no significant difference between the JC and fine-tuning models (p = 0.673). As our fine-tuning method requires fewer than 20 cases, this method is useful even in a facility where the number of glioma cases is small.
Keywords