Detection of Vestibular Schwannoma on Triple-parametric Magnetic Resonance Images Using Convolutional Neural Networks

Tzu Hsuan Huang, Wei Kai Lee, Chih Chun Wu, Cheng Chia Lee, Chia Feng Lu, Huai Che Yang, Chun Yi Lin, Wen Yuh Chung, Po Shan Wang, Yen Ling Chen, Hsiu Mei Wu, Wan You Guo, Yu Te Wu*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Purpose: The first step in typical treatment of vestibular schwannoma (VS) is to localize the tumor region, which is time-consuming and subjective because it relies on repeatedly reviewing different parametric magnetic resonance (MR) images. A reliable, automatic VS detection method can streamline the process. Methods: A convolutional neural network architecture, namely YOLO-v2 with a residual network as a backbone, was used to detect VS tumors from MR images. To heighten performance, T1-weighted–contrast-enhanced, T2-weighted, and T1-weighted images were combined into triple-channel images for feature learning. The triple-channel images were cropped into three sizes to serve as input images of YOLO-v2. The VS detection effectiveness levels were evaluated for two backbone residual networks that downsampled the inputs by 16 and 32. Results: The results demonstrated the VS detection capability of YOLO-v2 with a residual network as a backbone model. The average precision was 0.7953 for a model with 416 × 416-pixel input images and 16 instances of downsampling, when both the thresholds of confidence score and intersection-over-union were set to 0.5. In addition, under an appropriate threshold of confidence score, a high average precision, namely 0.8171, was attained by using a model with 448 × 448-pixel input images and 16 instances of downsampling. Conclusion: We demonstrated successful VS tumor detection by using a YOLO-v2 with a residual network as a backbone model on resized triple-parametric MR images. The results indicated the influence of image size, downsampling strategy, and confidence score threshold on VS tumor detection.

Original languageEnglish
Pages (from-to)626-635
Number of pages10
JournalJournal of Medical and Biological Engineering
Volume41
Issue number5
DOIs
StatePublished - Oct 2021

Keywords

  • Convolutional Neural Network
  • Multiparametric MR images
  • Tumor Detection
  • Vestibular Schwannoma
  • YOLO-v2

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