Pose-aware instance segmentation framework from cone beam CT images for tooth segmentation

Abstract

Individual tooth segmentation from cone beam computed tomography (CBCT) images is an essential prerequisite for an anatomical understanding of orthodontic structures in several applications, such as tooth reformation planning and implant guide simulations. However, the presence of severe metal artifacts in CBCT images hinders the accurate segmentation of each individual tooth. In this study, we propose a neural network for pixel-wise labeling to exploit an instance segmentation framework that is robust to metal artifacts. Our method comprises of three steps: 1) image cropping and realignment by pose regressions, 2) metal-robust individual tooth detection, and 3) segmentation. We first extract the alignment information of the patient by pose regression neural networks to attain a volume-of-interest (VOI) region and realign the input image, which reduces the interoverlapping area between tooth bounding boxes. Then, individual tooth regions are localized within a VOI realigned image using a convolutional detector. We improved the accuracy of the detector by employing non-maximum suppression and multiclass classification metrics in the region proposal network. Finally, we apply a convolutional neural network (CNN) to perform individual tooth segmentation by converting the pixel-wise labeling task to a distance regression task. Metal-intensive image augmentation is also employed for a robust segmentation of metal artifacts. The result shows that our proposed method outperforms other state-of-the-art methods, especially for teeth with metal artifacts. Our method demonstrated 5.68% and 30.30% better accuracy in the F1 score and aggregated Jaccard index, respectively, when compared to the best performing state-of-the-art algorithms. The primary significance of the proposed method is two-fold: 1) an introduction of pose-aware VOI realignment followed by a robust tooth detection and 2) a metal-robust CNN framework for accurate tooth segmentation.Index Terms-Cone beam computed tomography image segmentation, pose-aware tooth detection, pose regression neural network, tooth instance segmentation.

Applications of artificial intelligence and machine learning in orthodontics: a scoping review

“…For cases suitable for fixed mechanotherapy 1 [ 55 ] r. Selection of patients suitable to be treated with removable orthodontic appliances 1 [ 56 ] s. Class II division 1 malocclusion 1 [ 57 ] t. Broad-based 1 [ 79 ] Automated cephalometric landmarking and/or analysis and/or classification a. Lateral cephalogram 12 [ 27 , 32 , 35 , 37 , 64 , 65 , 69 , 70 , 74 , 75 , 77 , 78 ] b. CBCT images 6 [ 31 , 58 – 61 , 76 ] c. Frontal cephalogram 1 [ 19 ] Assessment of growth and development a. Cervical vertebra maturation 1 [ 18 ] b. Broad-based 3 [ 30 , 39 , 73 ] Evaluation of treatment outcome- orthognathic surgery on facial appearance/ attractiveness and/or age perception 2 [ 36 , 38 ] Miscellaneous a. Tooth segmentation from CBCT images/model 2 [ 33 , 43 ] b. Detection of activation pattern of tongue musculature 1 [ …”

“…Applications of AI and ML that could not be described under the above four major domains were grouped under the miscellaneous category in this review and these include automated tooth segmentation either from CBCT images or dental models [ 33 , 43 ], detection of activation pattern of tongue musculature [ 50 ] and evaluation of effects of a different curing unit and light-tips on temperature increase during orthodontic bonding [ 26 ].…”

Machine learning in orthodontics: Challenges and perspectives

“…25 Region-based CNN (R-CNN) is commonly utilized in object detection, and its pipelines include extracting region proposals, computing CNN features and classifying regions. 26 The most recent algorithm mainly improves proposed region localization efficiency, such as You Only Look Once (YOLO). 27 Moreover, it was discovered that a deep learning network based on YOLO was superior to oral surgeons in detecting odontogenic cysts in diagnostic function.…”

Machine learning methods as an aid in planning orthodontic treatment on the example of Cone-Beam Computed Tomography analysis: a literature review

“…The method is two-step: an edge detection of the CBCT and then a 3D region proposal module. This method is applicable for 3D data both quantitatively and qualitatively.Chung et al [18] created a method which includes metal artifacts. This was achieved by an improvement segmentation method on ToothNet.…”