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 FukaiYoshiko Okita, Naohiro Tsuyuguchi, Yonehiro Kanemura, Kazuma Kobayashi, Jun Sese, Koichi Ichimura, Yoshitaka Narita, Ryuji Hamamoto

研究成果: Contribution to journalArticle査読

1 被引用数 (Scopus)

抄録

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 < 0.0001) and the BraTS and fine-tuning models (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.

本文言語英語
論文番号1415
ページ(範囲)1-15
ページ数15
ジャーナルCancers
13
6
DOI
出版ステータス出版済み - 3 2 2021
外部発表はい

All Science Journal Classification (ASJC) codes

  • Oncology
  • Cancer Research

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