… In this paper, we propose a deep learning-based 3D volume segmentation framework to
address the above limitations. The proposed method is an end-to-end segmentation network …

… A convolutional U-net deep learning network was used to segment the trigeminal nerves
from the pons to the ganglion. A total of 216 radiomics features consisting of image texture, …

… a trigeminal nerve segmentation model using deep learning … trigeminal nerve using MR
imaging. The results show that, in comparison with the basic V-Net, our optimized deep learning …

… The emergence of AI-inspired deep learning methods such as convolution neural networks
(… These deep learning methods differ from traditional machine learning techniques such as …

… in examining peripheral V nerve characteristics. Mulford et al. employed a deep learning
network to segment and extract radiomic features from the pre-ganglionic V nerve to distinguish …

… This study aimed to implement a deep learning-based super-resolution (SR) technique
that can assist in the diagnosis and surgery of trigeminal neuralgia (TN) using magnetic …

… -based multi-class network for automated cranial nerves tract segmentation without … optic
nerve CN II, oculomotor nerve CN III, trigeminal nerve CN V, and facial–vestibulocochlear nerve …

… an automated system for image diagnosis of trigeminal neuralgia. Therefore, in this study, …
deep learning for AI diagnosis as fundamental research in this field. We discuss the learning …

… U-net deep learning network to segment the trigeminal nerve from the pons to the ganglion.
A radiomics approach was used to identify symptomatic trigeminal nerves from the MRIs of a …

… Although the focus of the study of TN has been primarily the peripheral segments of the
trigeminal nerve, we recently compared TN and MS-TN diffusivity differences in 4 segments of …