Cargando…

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

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 d...

Descripción completa

Detalles Bibliográficos
Autores principales: Lin, Jinghui, Mou, Lei, Yan, Qifeng, Ma, Shaodong, Yue, Xingyu, Zhou, Shengjun, Lin, Zhiqing, Zhang, Jiong, Liu, Jiang, Zhao, Yitian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8702731/
https://www.ncbi.nlm.nih.gov/pubmed/34955711
http://dx.doi.org/10.3389/fnins.2021.744967
_version_ 1784621306337034240
author Lin, Jinghui
Mou, Lei
Yan, Qifeng
Ma, Shaodong
Yue, Xingyu
Zhou, Shengjun
Lin, Zhiqing
Zhang, Jiong
Liu, Jiang
Zhao, Yitian
author_facet Lin, Jinghui
Mou, Lei
Yan, Qifeng
Ma, Shaodong
Yue, Xingyu
Zhou, Shengjun
Lin, Zhiqing
Zhang, Jiong
Liu, Jiang
Zhao, Yitian
author_sort Lin, Jinghui
collection PubMed
description 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.
format Online
Article
Text
id pubmed-8702731
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-87027312021-12-25 Automated Segmentation of Trigeminal Nerve and Cerebrovasculature in MR-Angiography Images by Deep Learning Lin, Jinghui Mou, Lei Yan, Qifeng Ma, Shaodong Yue, Xingyu Zhou, Shengjun Lin, Zhiqing Zhang, Jiong Liu, Jiang Zhao, Yitian Front Neurosci Neuroscience 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. Frontiers Media S.A. 2021-12-10 /pmc/articles/PMC8702731/ /pubmed/34955711 http://dx.doi.org/10.3389/fnins.2021.744967 Text en Copyright © 2021 Lin, Mou, Yan, Ma, Yue, Zhou, Lin, Zhang, Liu and Zhao. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Lin, Jinghui
Mou, Lei
Yan, Qifeng
Ma, Shaodong
Yue, Xingyu
Zhou, Shengjun
Lin, Zhiqing
Zhang, Jiong
Liu, Jiang
Zhao, Yitian
Automated Segmentation of Trigeminal Nerve and Cerebrovasculature in MR-Angiography Images by Deep Learning
title Automated Segmentation of Trigeminal Nerve and Cerebrovasculature in MR-Angiography Images by Deep Learning
title_full Automated Segmentation of Trigeminal Nerve and Cerebrovasculature in MR-Angiography Images by Deep Learning
title_fullStr Automated Segmentation of Trigeminal Nerve and Cerebrovasculature in MR-Angiography Images by Deep Learning
title_full_unstemmed Automated Segmentation of Trigeminal Nerve and Cerebrovasculature in MR-Angiography Images by Deep Learning
title_short Automated Segmentation of Trigeminal Nerve and Cerebrovasculature in MR-Angiography Images by Deep Learning
title_sort automated segmentation of trigeminal nerve and cerebrovasculature in mr-angiography images by deep learning
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8702731/
https://www.ncbi.nlm.nih.gov/pubmed/34955711
http://dx.doi.org/10.3389/fnins.2021.744967
work_keys_str_mv AT linjinghui automatedsegmentationoftrigeminalnerveandcerebrovasculatureinmrangiographyimagesbydeeplearning
AT moulei automatedsegmentationoftrigeminalnerveandcerebrovasculatureinmrangiographyimagesbydeeplearning
AT yanqifeng automatedsegmentationoftrigeminalnerveandcerebrovasculatureinmrangiographyimagesbydeeplearning
AT mashaodong automatedsegmentationoftrigeminalnerveandcerebrovasculatureinmrangiographyimagesbydeeplearning
AT yuexingyu automatedsegmentationoftrigeminalnerveandcerebrovasculatureinmrangiographyimagesbydeeplearning
AT zhoushengjun automatedsegmentationoftrigeminalnerveandcerebrovasculatureinmrangiographyimagesbydeeplearning
AT linzhiqing automatedsegmentationoftrigeminalnerveandcerebrovasculatureinmrangiographyimagesbydeeplearning
AT zhangjiong automatedsegmentationoftrigeminalnerveandcerebrovasculatureinmrangiographyimagesbydeeplearning
AT liujiang automatedsegmentationoftrigeminalnerveandcerebrovasculatureinmrangiographyimagesbydeeplearning
AT zhaoyitian automatedsegmentationoftrigeminalnerveandcerebrovasculatureinmrangiographyimagesbydeeplearning