Ensemble classification and segmentation for intracranial metastatic tumors on MRI images based on 2D U-nets

Cheng Chung Li, Meng Yun Wu, Ying Chou Sun, Hung Hsun Chen, Hsiu Mei Wu, Ssu Ting Fang, Wen Yuh Chung, Wan Yuo Guo, Henry Horng Shing Lu*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

The 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 75.64 % , while the result of segmentation achieves an IoU of 84.83 % and a DICE score of 86.21 %. Significantly reduce the time for manual labeling from 30 min to 18 s per patient.

Original languageEnglish
Article number20634
JournalScientific reports
Volume11
Issue number1
DOIs
StatePublished - Dec 2021

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