Automated Segmentation of Trigeminal Nerve and Cerebrovasculature in MR-Angiography Images by Deep Learning

Jinghui Lin, Lei Mou, Qifeng Yan, Shaodong Ma, Xingyu Yue, Shengjun Zhou, Zhiqing Lin, Jiong Zhang, Jiang Liu, Yitian Zhao

Research output: Journal PublicationArticlepeer-review

1 Citation (Scopus)


Trigeminal neuralgia caused by paroxysmal and severe pain in the distribution of the trigeminal nerve is a rare chronic pain disorder. It is generally accepted that compression of the trigeminal root entry zone by vascular structures is the major cause of primary trigeminal neuralgia, and vascular decompression is the prior choice in neurosurgical treatment. Therefore, accurate preoperative modeling/segmentation/visualization of trigeminal nerve and its surrounding cerebrovascular is important to surgical planning. In this paper, we propose an automated method to segment trigeminal nerve and its surrounding cerebrovascular in the root entry zone, and to further reconstruct and visual these anatomical structures in three-dimensional (3D) Magnetic Resonance Angiography (MRA). The proposed method contains a two-stage neural network. Firstly, a preliminary confidence map of different anatomical structures is produced by a coarse segmentation stage. Secondly, a refinement segmentation stage is proposed to refine and optimize the coarse segmentation map. To model the spatial and morphological relationship between trigeminal nerve and cerebrovascular structures, the proposed network detects the trigeminal nerve, cerebrovasculature, and brainstem simultaneously. The method has been evaluated on a dataset including 50 MRA volumes, and the experimental results show the state-of-the-art performance of the proposed method with an average Dice similarity coefficient, Hausdorff distance, and average surface distance error of 0.8645, 0.2414, and 0.4296 on multi-tissue segmentation, respectively.

Original languageEnglish
Article number744967
JournalFrontiers in Neuroscience
Publication statusPublished - 10 Dec 2021
Externally publishedYes


  • MRA
  • cerebrovascular
  • coarse-to-fine
  • deep learning
  • segmentation
  • trigeminal nerve

ASJC Scopus subject areas

  • Neuroscience (all)


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