Canal-Net for automatic and robust 3D segmentation of mandibular canals in CBCT images using a continuity-aware contextual network

• Published in: Scientific reports Vol. 12; no. 1; pp. 13460 - 11
• Main Authors: Jeoun, Bo-Soung, Yang, Su, Lee, Sang-Jeong, Kim, Tae-Il, Kim, Jun-Min, Kim, Jo-Eun, Huh, Kyung-Hoe, Lee, Sam-Sun, Heo, Min-Suk, Yi, Won-Jin
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 05.08.2022; Nature Publishing Group; Nature Portfolio
• Subjects: 631/114/1305; 692/698/3008; Accuracy; Bioengineering; Biological Phenomena; Cone-Beam Computed Tomography - methods; Cortical bone; Datasets; Deep learning; Dental research; Dentistry; Humanities and Social Sciences; Image processing; Image Processing, Computer-Assisted - methods; Long short-term memory; Mandibular Canal; Maxillofacial surgery; Mental task performance; multidisciplinary; Science; Science (multidisciplinary); Segmentation; Spiral Cone-Beam Computed Tomography; Temporal variations; Transplants & implants
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-022-17341-6

The purpose of this study was to propose a continuity-aware contextual network (Canal-Net) for the automatic and robust 3D segmentation of the mandibular canal (MC) with high consistent accuracy throughout the entire MC volume in cone-beam CT (CBCT) images. The Canal-Net was designed based on a 3D U-Net with bidirectional convolutional long short-term memory (ConvLSTM) under a multi-task learning framework. Specifically, the Canal-Net learned the 3D anatomical context information of the MC by incorporating spatio-temporal features from ConvLSTM, and also the structural continuity of the overall MC volume under a multi-task learning framework using multi-planar projection losses complementally. The Canal-Net showed higher segmentation accuracies in 2D and 3D performance metrics (p < 0.05), and especially, a significant improvement in Dice similarity coefficient scores and mean curve distance (p < 0.05) throughout the entire MC volume compared to other popular deep learning networks. As a result, the Canal-Net achieved high consistent accuracy in 3D segmentations of the entire MC in spite of the areas of low visibility by the unclear and ambiguous cortical bone layer. Therefore, the Canal-Net demonstrated the automatic and robust 3D segmentation of the entire MC volume by improving structural continuity and boundary details of the MC in CBCT images.