Three-Dimensional Cephalometric Landmark Annotation Demonstration on Human Cone Beam Computed Tomography Scans

Craniofacial cephalometric analysis is a diagnostic tool used for the assessment of the relationship of various bones and soft tissues in the head and ...

National Institute of Dental and Craniofacial Research

Morphological integration, or the tendency for covariation, is commonly seen in complex traits such as the human face. The effects of growth on shape, or allometry, represent a ubiquitous but poorly understood axis of integration. We address the question of to what extent age and measures of size converge on a single pattern of allometry for human facial shape.

Body size and allometric variation in facial shape in children.

In this paper, we propose a novel deep learning framework for anatomy segmentation and automatic landmarking. Specifically, we focus on the challenging problem of mandible segmentation from cone-beam computed tomography (CBCT) scans and identification of 9 anatomical landmarks of the mandible on the geodesic space. The overall approach employs three inter-related steps. In the first step, we propose a deep neural network architecture with carefully designed regularization, and network hyper-parameters to perform image segmentation without the need for data augmentation and complex post-processing refinement. In the second step, we formulate the landmark localization problem directly on the geodesic space for sparsely-spaced anatomical landmarks. In the third step, we utilize a long short-term memory network to identify the closely-spaced landmarks, which is rather difficult to obtain using other standard networks. The proposed fully automated method showed superior efficacy compared to the state-of-the-art mandible segmentation and landmarking approaches in craniofacial anomalies and diseased states. We used a very challenging CBCT data set of 50 patients with a high-degree of craniomaxillofacial variability that is realistic in clinical practice. The qualitative visual inspection was conducted for distinct CBCT scans from 250 patients with high anatomical variability. We have also shown the state-of-the-art performance in an independent data set from the MICCAI Head-Neck Challenge (2015).

Deep Geodesic Learning for Segmentation and Anatomical Landmarking.

Numerous 3d imaging systems are now available commercially for the capture of facial shape data via landmarking or surface shape comparisons but it is not known whether these systems produce data of comparable quality. This study investigates the error associated with landmark coordinate data collected on facial surface images taken using three 3d imaging systems: the 3dMDface system (3dMD, Atlanta, GA), the Planmeca ProFace system (Planmeca, Roselle, IL), and the Vectra H1 handheld system (Canfield Scientific, Parsippany, NJ). This was a retrospective study involving 3d imaging data that used geometric morphometric analysis to assess overall shape differences as well as landmark-specific differences among the systems. Ten individuals evaluated at the NIDCR dental clinic on various protocols were imaged on all 3 systems. The subject pool consisted of syndromic and unaffected subjects, as disease status was irrelevant to the question of reproducibility and variability. Variation in landmark placement across systems was assessed by ANOVA, covariance matrix, and summary statistics. No overall shape or size differences were found among the systems. However, there was some landmark-specific variation and the 3dMD and Vectra systems were generally more similar to each other than either was to the Planmeca system. The data acquired by these 3 systems are comparable, although landmarks on the eyes and ears are noisy and most different among systems.

Comparison of Three-Dimensional Surface Imaging Systems Using Landmark Analysis.

As cone-beam computed tomography (CBCT) scans become increasingly common, it is vital to have reliable 3-dimensional (3D) landmarks for quantitative analysis of craniofacial skeletal morphology. While some studies have developed and used 3D landmarks, these landmark sets are generally small and derived primarily from previous 2-dimensional (2D) cephalometric landmarks. These derived landmarks lack information in parts of the skull such as the cranial base, which is an important feature for cranial growth and development. The authors see a real need for development and validation of 3D landmarks, particularly bilateral landmarks, across the skull for improved cephalometric analysis. The primary objective of this study is to develop and validate a set of 61 3D anatomical landmarks on the face, cranial base, mandible, and teeth for use in clinical and research studies involving CBCT imaging. Each landmark was placed 3 times by 3 separate trained observers on a set of 10 anonymized CBCT patient scans. Intra-rater and inter-rater estimates of consistency and agreement were calculated using the intraclass correlation coefficient. Measurement error was calculated per landmark and per X, Y, and Z landmark coordinate. The authors had high ICC estimates within rates, indicating high consistency, and high ICC estimates among raters, indicate good agreement across raters. Overall measurement error for each landmark and each X, Y, and Z coordinate was low. Our results confirm the accuracy of novel 3D landmarks including several on the cranial base that will serve researchers and clinicians for use in future studies involving 3D CBCT imaging and craniofacial development.

Development and Validation of Novel Three-Dimensional Craniofacial Landmarks on Cone-Beam Computed Tomography Scans.

Elevated transforming growth factor-beta (TGF-β) signalling has been implicated in the pathogenesis of Loeys-Dietz syndrome (LDS) and Shprintzen-Goldberg syndrome (SGS). In this study, we provide a qualitative and quantitative analysis of the craniofacial and functional features among the LDS subtypes and SGS.

Loeys-Dietz and Shprintzen-Goldberg syndromes: analysis of TGF-β-opathies with craniofacial manifestations using an innovative multimodality method.

We perform anatomical landmarking for craniomaxillofacial (CMF) bones without explicitly segmenting them. Toward this, we propose a simple, yet efficient, deep network architecture, called relational reasoning network (RRN), to accurately learn the local and the global relations among the landmarks in CMF bones; specifically, mandible, maxilla, and nasal bones.

Denise K. Liberton

National Institute of Dental and Craniofacial Research

Three-Dimensional Cephalometric Landmark Annotation Demonstration on Human Cone Beam Computed Tomography Scans