Journal
JOURNAL OF COMPUTER ASSISTED TOMOGRAPHY
Volume 42, Issue 2, Pages 306-316Publisher
LIPPINCOTT WILLIAMS & WILKINS
DOI: 10.1097/RCT.0000000000000669
Keywords
image segmentation; surface reconstruction; mandible; automatic 3D segmentation; computed tomography; mandible development
Funding
- NIH from the National Institute on Deafness and other Communication Disorders [R01 DC006282]
- National Institute of Child Health and Human Development [P30 HD03352, U54 HD090256]
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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. Method 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. Conclusions Findings are indicative of a robust pipeline that reduces manual segmentation time by 75% and increases the feasibility of large-scale mandibular growth studies.
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