Anatomical Structure Sketcher for Cephalograms by Bimodal Deep Learning

Lateral cephalogram X-ray (LCX) images are essential to provide patientspecific morphological information of anatomical structures. The automatic annotation of anatomical structures in cephalograms has been performed in the biomedical engineering for nearly twenty years. Most systems only handle a portion of salient craniofacial landmark set [1, 2, 3]. Although model-based methods can produce a full set of markers [5, 7], the pattern fitting can fail to converge in blurry images. It is challenging to annotate LCX images with high fidelity. In this work, we propose a novel cephalogram sketcher system as shown in Fig. 1 for the automatic anatomical-structure annotation, especially for the blemished images due to structure overlappings and devicespecific distortions during projection. Firstly, we introduce an hierarchical extension of a pictorial model to detect anatomical structures. Secondly, the bimodal deep Boltzmann machine (DBM) is employed to sketch the structure contours. Specifically, the contour sketcher takes advantages of the path in the DBM to extract the contour definitions from the patch textures by alternating Gibbs sampling. Given a cephalogram I, the structure definition S, and the parameters Θ = (Θq,Θr) with respect to the intraand inter-layer correlations, the posterior probability distribution according to the Bayes rule is defined as P(S|I,Θ) ∝ P(I|S,Θ)P(S|Θ), where P(S|Θ) is a shape prior distribution. P(I|S,Θ) is the image likelihood given the hierarchical architecture and the model parameters. The likelihood can be factorized as a product of likelihoods of local structures.