• Cheng-Chung Li, Meng-Yun Wu, Ying-Chou Sun, Hung-Hsun Chen, Hsiu-Mei Wu, Ssu-Ting Fang, Wen-Yuh Chung, Wan-Yuo Guo, and Henry Horng-Shing Lu.
• Institute of Statistics, National Yang Ming Chiao Tung University, Hsinchu, Taiwan.
• Sci Rep. 2021 Oct 19; 11 (1): 20634.

AbstractThe extraction of brain tumor tissues in 3D Brain Magnetic Resonance Imaging (MRI) plays an important role in diagnosis before the gamma knife radiosurgery (GKRS). In this article, the post-contrast T1 whole-brain MRI images had been collected by Taipei Veterans General Hospital (TVGH) and stored in DICOM format (dated from 1999 to 2018). The proposed method starts with the active contour model to get the region of interest (ROI) automatically and enhance the image contrast. The segmentation models are trained by MRI images with tumors to avoid imbalanced data problem under model construction. In order to achieve this objective, a two-step ensemble approach is used to establish such diagnosis, first, classify whether there is any tumor in the image, and second, segment the intracranial metastatic tumors by ensemble neural networks based on 2D U-Net architecture. The ensemble for classification and segmentation simultaneously also improves segmentation accuracy. The result of classification achieves a F1-measure of [Formula: see text], while the result of segmentation achieves an IoU of [Formula: see text] and a DICE score of [Formula: see text]. Significantly reduce the time for manual labeling from 30 min to 18 s per patient.© 2021. The Author(s).

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