Magnetic Resonance Imaging Evaluation of Trigeminal Neuralgia

Kim, Hyungjin; Kim, Yi Kyung; Seong, Minjung

doi:10.1007/978-981-19-9171-4_6

Cited by 1 publication

(1 citation statement)

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“…MR neurography (MRN), dedicated to imaging peripheral nerves, provides a detailed map of neuromuscular anatomy and offers a non-invasive view of intraneural architecture in multiple orthogonal planes ( Kim et al, 2023 ). Furthermore, high-resolution MRI now provides exquisite anatomic detail, enabling radiologists to examine nearly the entire course of the trigeminal nerve, from its nuclei in the brainstem to the distal branches of its three main divisions: the ophthalmic, maxillary, and mandibular nerves ( Seeburg et al, 2016 ).…”

Section: Introductionmentioning

confidence: 99%

Deep learning-driven MRI trigeminal nerve segmentation with SEVB-net

Zhang,

Li,

Luo

et al. 2023

Front. Neurosci.

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PurposeTrigeminal neuralgia (TN) poses significant challenges in its diagnosis and treatment due to its extreme pain. Magnetic resonance imaging (MRI) plays a crucial role in diagnosing TN and understanding its pathogenesis. Manual delineation of the trigeminal nerve in volumetric images is time-consuming and subjective. This study introduces a Squeeze and Excitation with BottleNeck V-Net (SEVB-Net), a novel approach for the automatic segmentation of the trigeminal nerve in three-dimensional T2 MRI volumes.MethodsWe enrolled 88 patients with trigeminal neuralgia and 99 healthy volunteers, dividing them into training and testing groups. The SEVB-Net was designed for end-to-end training, taking three-dimensional T2 images as input and producing a segmentation volume of the same size. We assessed the performance of the basic V-Net, nnUNet, and SEVB-Net models by calculating the Dice similarity coefficient (DSC), sensitivity, precision, and network complexity. Additionally, we used the Mann–Whitney U test to compare the time required for manual segmentation and automatic segmentation with manual modification.ResultsIn the testing group, the experimental results demonstrated that the proposed method achieved state-of-the-art performance. SEVB-Net combined with the ωDoubleLoss loss function achieved a DSC ranging from 0.6070 to 0.7923. SEVB-Net combined with the ωDoubleLoss method and nnUNet combined with the DoubleLoss method, achieved DSC, sensitivity, and precision values exceeding 0.7. However, SEVB-Net significantly reduced the number of parameters (2.20 M), memory consumption (11.41 MB), and model size (17.02 MB), resulting in improved computation and forward time compared with nnUNet. The difference in average time between manual segmentation and automatic segmentation with manual modification for both radiologists was statistically significant (p < 0.001).ConclusionThe experimental results demonstrate that the proposed method can automatically segment the root and three main branches of the trigeminal nerve in three-dimensional T2 images. SEVB-Net, compared with the basic V-Net model, showed improved segmentation performance and achieved a level similar to nnUNet. The segmentation volumes of both SEVB-Net and nnUNet aligned with expert annotations but SEVB-Net displayed a more lightweight feature.

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Section: Introductionmentioning

confidence: 99%