Development of a fully automated glioma-grading pipeline using post-contrast t1-weighted images combined with cloud-based 3d convolutional neural network

Hiroto Yamashiro, Atsushi Teramoto, Kuniaki Saito, Hiroshi Fujita

研究成果: Article査読

3 被引用数 (Scopus)

抄録

Glioma is the most common type of brain tumor, and its grade influences its treatment policy and prognosis. Therefore, artificial-intelligence-based tumor grading methods have been studied. However, in most studies, two-dimensional (2D) analysis and manual tumor-region extraction were performed. Additionally, deep learning research that uses medical images experiences difficulties in collecting image data and preparing hardware, thus hindering its widespread use. Therefore, we developed a 3D convolutional neural network (3D CNN) pipeline for realizing a fully automated glioma-grading system by using the pretrained Clara segmentation model provided by NVIDIA and our original classification model. In this method, the brain tumor region was extracted using the Clara segmentation model, and the volume of interest (VOI) created using this extracted region was assigned to a grading 3D CNN and classified as either grade II, III, or IV. Through evaluation using 46 regions, the grading accuracy of all tumors was 91.3%, which was comparable to that of the method using multi-sequence. The proposed pipeline scheme may enable the creation of a fully automated glioma-grading pipeline in a single sequence by combining the pretrained 3D CNN and our original 3D CNN.

本文言語English
論文番号5118
ジャーナルApplied Sciences (Switzerland)
11
11
DOI
出版ステータスPublished - 01-06-2021

All Science Journal Classification (ASJC) codes

  • 材料科学(全般)
  • 器械工学
  • 工学(全般)
  • プロセス化学およびプロセス工学
  • コンピュータ サイエンスの応用
  • 流体および伝熱

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