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...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2021-12-01
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| Series: | Frontiers in Neuroscience |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnins.2021.744967/full |
| _version_ | 1818721791166644224 |
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| author | Jinghui Lin Lei Mou Lei Mou Qifeng Yan Shaodong Ma Xingyu Yue Shengjun Zhou Zhiqing Lin Jiong Zhang Jiang Liu Yitian Zhao Yitian Zhao |
| author_facet | Jinghui Lin Lei Mou Lei Mou Qifeng Yan Shaodong Ma Xingyu Yue Shengjun Zhou Zhiqing Lin Jiong Zhang Jiang Liu Yitian Zhao Yitian Zhao |
| author_sort | Jinghui Lin |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-17T20:44:20Z |
| format | Article |
| id | doaj.art-4378e537fa934e4796ef97d5c57ae73c |
| institution | Directory Open Access Journal |
| issn | 1662-453X |
| language | English |
| last_indexed | 2024-12-17T20:44:20Z |
| publishDate | 2021-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroscience |
| spelling | doaj.art-4378e537fa934e4796ef97d5c57ae73c2022-12-21T21:33:12ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2021-12-011510.3389/fnins.2021.744967744967Automated Segmentation of Trigeminal Nerve and Cerebrovasculature in MR-Angiography Images by Deep LearningJinghui Lin0Lei Mou1Lei Mou2Qifeng Yan3Shaodong Ma4Xingyu Yue5Shengjun Zhou6Zhiqing Lin7Jiong Zhang8Jiang Liu9Yitian Zhao10Yitian Zhao11Department of Neurosurgery, Ningbo First Hospital, Ningbo, ChinaCixi Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, ChinaUniversity of Chinese Academy of Sciences, Beijing, ChinaCixi Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, ChinaCixi Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, ChinaCixi Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, ChinaDepartment of Neurosurgery, Ningbo First Hospital, Ningbo, ChinaDepartment of Neurosurgery, Ningbo First Hospital, Ningbo, ChinaCixi Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, ChinaDepartment of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, ChinaThe Affiliated People's Hospital of Ningbo University, Ningbo, ChinaCixi Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, ChinaTrigeminal 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.https://www.frontiersin.org/articles/10.3389/fnins.2021.744967/fulltrigeminal nervecerebrovascularsegmentationMRAdeep learningcoarse-to-fine |
| spellingShingle | Jinghui Lin Lei Mou Lei Mou Qifeng Yan Shaodong Ma Xingyu Yue Shengjun Zhou Zhiqing Lin Jiong Zhang Jiang Liu Yitian Zhao Yitian Zhao Automated Segmentation of Trigeminal Nerve and Cerebrovasculature in MR-Angiography Images by Deep Learning Frontiers in Neuroscience trigeminal nerve cerebrovascular segmentation MRA deep learning coarse-to-fine |
| 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 | trigeminal nerve cerebrovascular segmentation MRA deep learning coarse-to-fine |
| url | https://www.frontiersin.org/articles/10.3389/fnins.2021.744967/full |
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