Landmark localization in images with variable Field-Of-View

The goal of this work is to accurately localize anatomical structures in 3D Computed Tomography (CT) scans. Clinical protocols vary greatly in the scanned Field-Of-View (FOV), hence a typical scan may contain only a subset of a larger group of anatomical landmarks learned from the whole body. We extend parts-based graphical models for landmark localization in such partial FOV scans, by augmenting the state space with extra states with learnt penalties, which account for placements of each invisible landmark outside of the FOV. The resulting algorithm maintains the attractive computational properties of the original approach, whilst achieving accurate localization of the visible landmarks. We report results for localizing 11 standard anatomical landmarks (skeletal and soft tissue), using 3,000 truncated 3D CT images from a database of lung cancer patients. We compare our method against a baseline graphical model and report landmark localization error reduced on average by 54%, for a wide range of previously unknown FOV sizes.