three-dimensional-cephalometric-landmark-annotation-demonstration-on

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

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.

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.

A significant amount of clinical information captured as free-text narratives could be better used for several applications, such as clinical decision support, ontology development, evidence-based practice, and research. The Human Phenotype Ontology (HPO) is specifically used for semantic comparisons for diagnostic purposes. All these functions require quality coverage of the domain of interest. The authors used natural language processing to capture craniofacial and oral phenotype signatures from electronic health records and then used these signatures for evaluation of existing oral phenotype ontology coverage.

Data-driven method to enhance craniofacial and oral phenotype vocabularies.

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.

Article	Year
Deep Geodesic Learning for Segmentation and Anatomical Landmarking. IEEE transactions on medical imaging\| PubMed ID: 30334750	2019
Development and Validation of Novel Three-Dimensional Craniofacial Landmarks on Cone-Beam Computed Tomography Scans. The Journal of craniofacial surgery\| PubMed ID: 31478954	2019
Data-driven method to enhance craniofacial and oral phenotype vocabularies. Journal of the American Dental Association (1939)\| PubMed ID: 31668172	2019
Relational reasoning network for anatomical landmarking. Journal of medical imaging (Bellingham, Wash.)\| PubMed ID: 36891503	2023

Payal Verma

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