Automatic craniomaxillofacial landmark digitization via segmentation-guided partially-joint regression forest model

Abstract

Craniomaxillofacial (CMF) deformities involve congenital and acquired deformities of the head and face. Landmark digitization is a critical step in quantifying CMF deformities. In current clinical practice, CMF landmarks have to be manually digitized on 3D models, which is time-consuming. To date, there is no clinically acceptable method that allows automatic landmark digitization, due to morphological variations among different patients and artifacts of cone-beam computed tomography (CBCT) images. To address these challenges, we propose a segmentation-guided partially-joint regression forest model that can automatically digitizes CMF landmarks. In this model, a regression voting strategy is first adopted to localize landmarks by aggregating evidences from context locations, thus potentially relieving the problem caused by image artifacts near the landmark. Second, segmentation is also utilized to resolve inconsistent landmark appearances that are caused by morphological variations among different patients, especially on the teeth. Third, a partially-joint model is proposed to separately localize landmarks based on coherence of landmark positions to improve digitization reliability. The experimental results show that the accuracy of automatically digitized landmarks using our approach is clinically acceptable.