Automated identification and quantification of metastatic brain tumors and perilesional edema based on a deep learning neural network

Chi Jen Chou, Huai Che Yang, Po Yao Chang, Ching Jen Chen, Hsiu Mei Wu, Chun Fu Lin, I. Chun Lai, Syu Jyun Peng

研究成果: 雜誌貢獻文章同行評審

摘要

Purpose: This paper presents a deep learning model for use in the automated segmentation of metastatic brain tumors and associated perilesional edema. Methods: The model was trained using Gamma Knife surgical data (90 MRI sets from 46 patients), including the initial treatment plan and follow-up images (T1-weighted contrast-enhanced (T1cWI) and T2-weighted images (T2WI)) manually annotated by neurosurgeons to indicate the target tumor and edema regions. A mask region-based convolutional neural network was used to extract brain parenchyma, after which the DeepMedic 3D convolutional neural network was in the segmentation of tumors and edemas. Results: Five-fold cross-validation demonstrated the efficacy of the brain parenchyma extraction model, achieving a Dice similarity coefficient of 96.4%. The segmentation models used for metastatic tumors and brain edema achieved Dice similarity coefficients of 71.6% and 85.1%, respectively. This study also presents an intuitive graphical user interface to facilitate the use of these models in clinical analysis. Conclusion: This paper introduces a deep learning model for the automated segmentation and quantification of brain metastatic tumors and perilesional edema trained using only T1cWI and T2WI. This technique could facilitate further research on metastatic tumors and perilesional edema as well as other intracranial lesions.
原文英語
頁(從 - 到)167-174
頁數8
期刊Journal of Neuro-Oncology
166
發行號1
DOIs
出版狀態已發佈 - 1月 2024

ASJC Scopus subject areas

  • 腫瘤科
  • 神經內科
  • 神經病學(臨床)
  • 癌症研究

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