[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.

Micro-Computed Tomography-Guided Artificial Intelligence for Pulp Cavity and Tooth Segmentation on Cone-beam Computed Tomography.

Introduction: This study proposes a novel data pipeline based on micro-computed tomographic (micro-CT) data for training the U-Net network to realize the automatic and accurate segmentation of the pulp cavity and tooth on cone-beam computed tomographic (CBCT) images.

Methods: We collected CBCT data and micro-CT data of 30 teeth. CBCT data were processed and transformed into small field of view and high-resolution CBCT images of each tooth.

Analysis of segmentation of lung parenchyma based on deep learning methods.

Precise segmentation of lung parenchyma is essential for effective analysis of the lung. Due to the obvious contrast and large regional area compared to other tissues in the chest, lung tissue is less difficult to segment. Special attention to details of lung segmentation is also needed.