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In this study, the authors developed a deep feature fusion model (DFFM) in order to segment postoperative gliomas on CT images, which were guided by multi-sequence MRIs. The authors found that DFFM enabled accurate segmentation of CT postoperative gliomas, which may help to improve radiotherapy planning.

Key points

  • A fully automated deep learning method was developed to segment postoperative gliomas on CT images guided by multi-sequence MRIs.
  • CT and multi-sequence MR image integration allows for improvements in deep learning postoperative glioma segmentation method.
  • This deep feature fusion model produces reliable segmentation results and could be useful in delineating GTV in postoperative glioma radiotherapy planning.

Article: Postoperative glioma segmentation in CT image using deep feature fusion model guided by multi-sequence MRIs

Authors: Fan Tang, Shujun Liang, Tao Zhong, Xia Huang, Xiaogang Deng, Yu Zhang & Linghong Zhou

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