Detection of Vestibular Schwannoma on Triple-parametric Magnetic Resonance Images Using Convolutional Neural Networks
| العنوان: | Detection of Vestibular Schwannoma on Triple-parametric Magnetic Resonance Images Using Convolutional Neural Networks |
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| المؤلفون: | Cheng-Chia Lee, Po Shan Wang, Wen Yuh Chung, Tzu Hsuan Huang, Huai-Che Yang, Yu Te Wu, Wei Kai Lee, Chih Chun Wu, Hsiu Mei Wu, Chia Feng Lu, Chun Yi Lin, Wan You Guo, Yen Ling Chen |
| المصدر: | Journal of Medical and Biological Engineering. |
| بيانات النشر: | Springer Science and Business Media LLC, 2021. |
| سنة النشر: | 2021 |
| مصطلحات موضوعية: | medicine.diagnostic_test, Computer science, business.industry, Biomedical Engineering, Process (computing), Pattern recognition, Magnetic resonance imaging, General Medicine, Residual, Convolutional neural network, 030218 nuclear medicine & medical imaging, Upsampling, 03 medical and health sciences, 0302 clinical medicine, medicine, Artificial intelligence, business, Feature learning, Image resolution, 030217 neurology & neurosurgery, Parametric statistics |
| الوصف: | 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. |
| تدمد: | 2199-4757 1609-0985 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_________::67fa4e4783a629981a8777a8b6804f16 https://doi.org/10.1007/s40846-021-00638-8 |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi...........67fa4e4783a629981a8777a8b6804f16 |
| قاعدة البيانات: | OpenAIRE |
Find this article in full text from Springer Verlag. | تدمد: | 21994757 16090985 |
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