A Hierarchical 3D Segmentation Model for Cone-Beam Computed Tomography Dental-Arch Scans

In this paper we define a deep learning architecture, for automated segmentation of anatomical structures in Craniomaxillofacial (CMF) CT images that leverages the recent success of encoder-decoder models for semantic segmentation of medical images. The aim of this work is to propose an architecture capable to perform the automated segmentation of the dental arch from CBCT scans Cranio-Maxillo-Facial, offering a fast, efficient and reliable method of obtaining images labeled. A deep convolutional neural network was applied by exploiting the deep supervision mechanism with the aim of train a model for the extraction of feature maps at different levels of abstraction, to obtain an accurate segmentation of the images passed in input. In particular, we propose a 3D CNN-based architecture for automated segmentation from CT scans, with a 3D encoder that learns to extract features at different levels of abstraction and send them hierarchically to four 3D decoders that predict intermediate segmentation maps used to obtain the final detailed binary mask. The automated segmentation model is tested with an error, on average, of 0.2%.