Objective: We present a registration-based semiautomatic mandible segmentation (SAMS) pipeline designed to process a large number of computed tomography studies to segment 3-dimensional mandibles. The pipeline consists of a manual preprocessing step, an automatic segmentation step, and a final manual postprocessing step. The automatic portion uses a nonlinear diffeomorphic method to register each preprocessed input computed tomography test scan on 54 reference templates, ranging in age from birth to 19 years. This creates 54 segmentations, which are then combined into a single composite mandible. Results: This pipeline was assessed using 20 mandibles from computed tomography studies with ages 1 to 19 years, segmented using both SAMS-processing and manual segmentation. Comparisons between the SAMS-processed and manually-segmented mandibles revealed 97% similarity agreement with comparable volumes. The resulting 3-dimensional mandibles were further enhanced with manual postprocessing in specific regions. Findings are indicative of a robust pipeline that reduces manual segmentation time by 75% and increases the feasibility of largescale mandibular growth studies. Key Words: image segmentation, surface reconstruction, mandible, automatic 3D segmentation, computed tomography, mandible development (J Comput Assist Tomogr 2018;42: 306-316) W ith the advent of modern medical imaging technologies, automatic and semiautomatic methods have been developed to segment mandible volumes from medical imaging studies. Such computer-assisted segmentation methods help reduce the time needed to create precise and accurate 3-dimensional (3D) mandible models, and can be particularly beneficial for largescale studies. These methods have been devised for application in dental surgical/treatment planning, 1,2 orthodontic diagnoses, 3 and dental forensics 4 to reconstruct 3D mandible models from imaging studies such as panoramic x-rays, cone-beam computed tomography, and multidetector computed tomography (MDCT). To study mandibular growth, a handful of studies have used 3D models by manually segmenting mandibles from MDCT. [5] [6] [7] [8] Most of these studies have used small age ranges and a restricted sample size. However, to enable a lifespan perspective on sexspecific mandibular growth including changes in surface area or regions of bone deposition or resorption, 9 a large number of 3D mandible models covering the entire age range is needed. Such a perspective can also help establish a developmental age and sex-specific normative reference, including a range of variability in typical growth, for use in diagnostics and/or treatment such as for application in orthodontic treatment planning 10 or craniofacial surgical planning. 11 To our knowledge, there has been only 1 mandibular development study using reconstructed 3D mandibles. 6 The heterogeneity of the mandibular morphology across individuals and age groups makes 3D mandible models difficult to extract from computed tomography (CT) studies. The temporomandibular joint (TMJ) and dentition are often omitted from computer-assisted segmentation techniques because of difficulties in separating the condyle from the temporal bone and the dentition from its imaging artifacts. Because the growth and shifting of these structures affect mandibular growth and displacement, it is important to include them in mandibular growth studies and to establish a segmentation protocol that can segment the entire mandible. Given the morphological variations across individuals, manual segmentation of the entire mandible to generate 3D mandible models has been considered the criterion standard in both clinical and research settings. 4 With this method, a human segmenter extracts the mandible from CT images through manual editing after 3D renderings or on a slice-by-slice basis. Manual segmentation requires 2 to 3 hours per mandible for a trained researcher to produce a complete 3D mandible model, and it is susceptible to interresearcher and intraresearcher variability. Thus, as noted earlier, manual segmentation is suboptimal for studying mandibular growth and development where large sample sizes are needed. Both automatic and semiautomatic mandible segmentation (SAMS) techniques reduce potential human variability and error and speeds up the 3D segmentation process because human manual intervention or supervision is either not required (automatic segmentation) or limited (semiautomatic) to manual correction of segmentation errors. 3, 14, 15 However, mandibular-focused automatic segmentation techniques, such as threshold based, region growing, gray-level detection, active contour models, and statistical shape models, often produce inaccurate segmentation of the teeth and the condyles, and some demonstrate only partial mandible delineation. 2, [15] [16] [17] Similarly, the available semiautomatic techniques are not particularly robust. 3, 14 Studies using automatic or SAMS of images acquired using cone-beam computed tomography [16] [17] [18] produce 3D models that are prone to low image quality and artifacts. In contrast, MDCT generates 3D models that show precise bony structures with high From the *Vocal
A registration-based semiautomatic mandible segmentation technique was proposed by Chuang el REF in 2017.
A Novel Registration-Based Semiautomatic Mandible Segmentation Pipeline Using Computed Tomography Images to Study Mandibular Development
Journal of Computer Assisted Tomography
Medicine