Patient Surface Model and Internal Anatomical Landmarks Embedding

Abstract

The patient surface model has shown to be a useful asset to improve existing diagnostic and interventional tasks in a clinical environment. For example, in combination with RGB-D cameras, a patient surface model can be used to automate and accelerate the diagnostic imaging workflow, manage patient dose, and provide navigation assistance. A shortcoming of today’s patient surface models, however, is that, internal anatomical landmarks are not present. In this paper, we introduce a method to estimate internal anatomical landmarks based on the surface model of a patient. Our method relies on two major steps. First, we fit a template surface model is to a segmented surface of a CT dataset with annotated internal landmarks using keypoint and feature descriptor based rigid alignment and atlas-based non-rigid registration. In a second step, we find for each internal landmark a neighborhood on the template surface and learn a generalized linear embedding between neighboring surface vertices in the template and the internal landmark. We trained and evaluated our method using cross-validation in 20 datasets over 50 internal landmarks. We compared the performance of four different generalized linear models. The best mean estimation error over all the landmarks was achieved using the lasso regression method with a mean error of 12.19 ± 6.98 mm.