Two-Stage 3D U-Net-Based Segmentation Of Maxillofacial Structures From CBCT Scans For Dental Implant Planning

Abstract

This work represents a 3D segmentation framework for automatic landmark delineation in Cone Beam Computed Tomography to aid dental implant planning. The proposed method aimed to segment important anatomical details like bone, nerve canals, and teeth using the Bone segmentation algorithm, Deep Neural Network Based Minimal Medical segmentation, and Tooth segmentation method, respectively. The bone segmentation algorithm utilizes two-stage 3D U-Net architecture where coarse global segmentation combines with fine segmentation for accurate detection of anatomical structures like tooth, alveolar ridge, maxillary sinus and nasal cavity. Preprocessing of CBCT images is done preceding to segmentation to remove poisonous noise using PNR algorithm, Thermal noise using TFR algorithm and ring artifacts with RAR algorithm. The regions of interest identified in segmentation phase leads to exact measurements of important anatomical structures that further assists in automation of medical report generation system. The assessment of segmentation algorithms is done using parameters such as DICE-Score and IoU. Our proposed system achieved 0.96, 0.97, 0.92, 0.96 and 0.99 DICE-Score values for mandible, maxilla, nerve canal, teeth and background respectively as well as 0.93, 0.94, 0.86, 0.93 and 0.99 IoU score values for mandible, maxilla, nerve canal, teeth and background respectively.