Efficacy evaluation of 2D, 3D U-Net semantic segmentation and atlas-based segmentation of normal lungs excluding the trachea and main bronchi

Takafumi Nemoto, Natsumi Futakami, Masamichi Yagi, Atsuhiro Kumabe, Atsuya Takeda, Etsuo Kunieda, Naoyuki Shigematsu

研究成果: Article査読

11 被引用数 (Scopus)

抄録

This study aimed to examine the efficacy of semantic segmentation implemented by deep learning and to confirm whether this method is more effective than a commercially dominant auto-segmentation tool with regards to delineating normal lung excluding the trachea and main bronchi. A total of 232 non-small-cell lung cancer cases were examined. The computed tomography (CT) images of these cases were converted from Digital Imaging and Communications in Medicine (DICOM) Radiation Therapy (RT) formats to arrays of 32 × 128 × 128 voxels and input into both 2D and 3D U-Net, which are deep learning networks for semantic segmentation. The number of training, validation and test sets were 160, 40 and 32, respectively. Dice similarity coefficients (DSCs) of the test set were evaluated employing Smart Segmentation® Knowledge Based Contouring (Smart segmentation is an atlas-based segmentation tool), as well as the 2D and 3D U-Net. The mean DSCs of the test set were 0.964 [95% confidence interval (CI), 0.960-0.968], 0.990 (95% CI, 0.989-0.992) and 0.990 (95% CI, 0.989-0.991) with Smart segmentation, 2D and 3D U-Net, respectively. Compared with Smart segmentation, both U-Nets presented significantly higher DSCs by the Wilcoxon signed-rank test (P < 0.01). There was no difference in mean DSC between the 2D and 3D U-Net systems. The newly-devised 2D and 3D U-Net approaches were found to be more effective than a commercial auto-segmentation tool. Even the relatively shallow 2D U-Net which does not require high-performance computational resources was effective enough for the lung segmentation. Semantic segmentation using deep learning was useful in radiation treatment planning for lung cancers.

本文言語English
ページ(範囲)257-264
ページ数8
ジャーナルJournal of radiation research
61
2
DOI
出版ステータスPublished - 2020 3 23

ASJC Scopus subject areas

  • 放射線
  • 放射線学、核医学およびイメージング
  • 健康、毒物学および変異誘発

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