[Segmentation and accuracy validation of mandibular molar and pulp cavity on cone-beam CT images by U-net neural network].

Purpose: To realize the automatic segmentation of mandibular molar and pulp cavity on cone-beam CT (CBCT) images by U-net convolutional neural network, and to use the 3D models reconstructed by Micro-CT data as the ground truth to validate its accuracy. METHODS： Twenty groups of small field of view(FOV) CBCT data containing complete mandibular molars were collected from the Department of Radiology, Affiliated Stomatology Hospital of Tongji University. After preprocessing, an endodontic specialist labeled teeth and pulp cavities by MITK Workbench software.