摘要
Accurate localization of cranial nerves and responsible blood vessels is important for diagnosing trigeminal neuralgia(TN)and hemifacial spasm(HFS).Manual delineation of the nerves and vessels on medical images is time-consuming and labor-intensive.Due to the development of convolutional neural networks(CNNs),the performance of medical image segmentation has been improved.In this work,we investigate the plans for automated segmentation of cranial nerves and responsible vessels for TN and HFS,which has not been comprehensively studied before.Different inputs are given to the CNN to find the best training configuration of segmenting trigeminal nerves,facial nerves,responsible vessels and brainstem,including the image modality and the number of segmentation targets.According to multiple experiments with seven training plans,we suggest training with the combination of three-dimensional fast imaging employing steady-state acquisition(3D-FIESTA)and three-dimensional time-of-flight magnetic resonance angiography(3DTOF-MRA),and separate segmentation of cranial nerves and vessels.
