Automatic Detection of Anatomical Landmarks on Geometric Mesh Data using Deep Semantic Segmentation

Anatomical landmark detection is the first step towards the analysis of 3D medical data. In this paper, we annotate the biologically significant landmarks on sphere-like meshes using deep semantic segmentation. A triplet candidate pool and the cutting path are firstly defined to parameterize 3D mesh model into 2D planar flat-torus. A deep convolutional network is utilized to learn geometric surface properties and then segment landmark areas. The landmarks are finally localized within their areas by incorporating the local neighborhood features. Extensive experiments are conducted on our newly-constructed scapula dataset, where we demonstrate the accuracy and efficacy of the proposed approach.