Deep Geodesic Learning for Segmentation and Anatomical Landmarking

The ultimate goal of clinicians is to provide accurate and rapid clinical interpretation, which guides appropriate treatment of CMF deformities. Cone-beam computed tomography (CBCT) is the newest conventional imaging modality for the diagnosis and treatment planning of patients with skeletal CMF deformities. Not only do CBCT scanners expose patients to lower doses of radiation compared to spiral CT scanners, but also CBCT scanners are compact, fast and less expensive, which makes them widely available. On the other hand, CBCT scans have much greater noise and artifact presence, leading to challenges in image analysis tasks. CBCT-based image analysis plays a significant role in diagnosing a disease or deformity, characterizing its severity, planning the treatment options, and estimating the risk of potential interventions. The core image analysis framework involves the detection and measurement of deformities, which requires precise segmentation of CMF bones. Landmarks, which identify anatomically distinct locations on the surface of the segmented bones, are placed and measurements are performed to determine the severity of the deformity compared to traditional 2D norms as well as to assist in treatment and surgical planning. Figure 1 shows nine anatomical landmarks defined on the mandible. Surgical planning, patient-specific prediction of deformities, and quantification as well as clinical assessment of the deformities require precise segmentation and anatomical landmarking. However, automatically segmenting bones from the CMF regions, and accurately identifying clinically relevant anatomical landmarks on the surface of these bones continue to be a significant challenge and a persistent problem. Currently, the landmarks have not evolved from traditional 2D anatomical landmarks for cephalometric analysis though 3D imaging has become more commonplace for clinical application. Additionally, landmarking on CT images is tedious and manual or semi-automated and prone to operator variability. Despite some recent elaborative efforts towards making a fully automated and accurate software for segmentation of bones and landmarking for deformation analysis in dental applications [3], [4], the problem remains largely unsolved for global CMF deformity analysis, especially for those who have congenital or developmental deformities for whom the diagnosis and treatment planning are most critically needed. The main reason for this research gap is high anatomical variability in the shape of these bones due to their deformities in such patient populations. Abstract--In this paper, we propose a novel deep learning framework for anatomy segmentation and automatic landmarking.