  We propose a multi-stage coarse-to-fine CNN-based framework, called
SkullEngine, for high-resolution segmentation and large-scale landmark
detection through a collaborative, integrated, and scalable JSD model and three
segmentation and landmark detection refinement models. We evaluated our
framework on a clinical dataset consisting of 170 CBCT/CT images for the task
of segmenting 2 bones (midface and mandible) and detecting 175 clinically
common landmarks on bones, teeth, and soft tissues.
