Alveolar Bone Segmentation in Intraoral Ultrasonographs with Machine Learning

Abstract

The use of intraoral ultrasound imaging has received great attention recently due to the benefits of being a portable and low-cost imaging solution for initial and continuing care that is noninvasive and free of ionizing radiation. Alveolar bone is an important structure in the periodontal apparatus to support the tooth. Accurate assessment of alveolar bone level is essential for periodontal diagnosis. However, interpretation of alveolar bone structure in ultrasound images is a challenge for clinicians. This work is aimed at automatically segmenting alveolar bone and locating the alveolar crest via a machine learning (ML) approach for intraoral ultrasound images. Three convolutional neural network–based ML methods were trained, validated, and tested with 700, 200, and 200 images, respectively. To improve the robustness of the ML algorithms, a data augmentation approach was introduced, where 2100 additional images were synthesized through vertical and horizontal shifting as well as horizontal flipping during the training process. Quantitative evaluations of 200 images, as compared with an expert clinician, showed that the best ML approach yielded an average Dice score of 85.3%, sensitivity of 88.5%, and specificity of 99.8%, and identified the alveolar crest with a mean difference of 0.20 mm and excellent reliability (intraclass correlation coefficient ≥0.98) in less than a second. This work demonstrated the potential use of ML to assist general dentists and specialists in the visualization of alveolar bone in ultrasound images.

Multiclass CBCT Image Segmentation for Orthodontics with Deep Learning

“…Deep convolutional neural networks (CNNs) have recently become increasingly utilized in medical image segmentation (Litjens et al 2017; Altaf et al 2019) and have achieved state-of-the-art performances (Minnema et al 2018; Casalegno et al 2019; Nguyen et al 2020). The success of CNNs can be mainly attributed to their ability of learning nonlinear spatial features in input images.…”

Three-dimensional periodontal investigations using a prototype handheld ultrasound scanner with spatial positioning reading sensor

“…Xu et al [24] traced the contour in the US tongue image sequence using the active contour method with an automatic re-initialization technique. Chifor et al [25] explored the usage of US in the detection of gingival sulcus with the assistance of active contour based semiautomatic sulcus segmentation in the swine specimen; Nguyen et al [26] segmented alveolar bone in US images using the graph-cut based method with mandibular incisors from piglets, and subsequently adopted a deep learning approach to detect alveolar bone in humans [4].…”

“…Noninvasive techniques to image gingival soft tissues are not currently available in dentistry. Bi-dimensional ultrasound (2D US) imaging has already shown great potential for evaluating periodontal soft-tissue structures [2][3][4][5]. Lin et al demonstrated that periodontal probing depths could be measured with photoacoustic imaging [6].…”

Computer-Assisted Detection of Cemento-Enamel Junction in Intraoral Ultrasonographs

“…Ultrasound scanning was performed at 20 MHz using a SonixTablet portable medical ultrasonic system (Analogic, Vancouver, BC, Canada) and a linear phased array transducer (L40-8/12, Analogic, Vancouver, Canada). The long axis of the transducer was positioned on a piece of 5 mm-thick gelatin-based gel pad located on the labial side of the tooth and gingiva in alignment with the longitudinal axis of the tooth [21]. The gel pad also functioned to ensure the region of interest (ROI) within the focal zone of the ultrasound beam for optimum resolution.…”

“…The applications and development of ultrasound in dentistry, especially in periodontics, have been extensively reported [14][15][16][17][18][19][20][21][22][23]. Ultrasound is a noninvasive, ionizingradiation-free, economical, and portable diagnostic tool for hard and soft tissue imaging.…”