Localization and labeling of posterior ribs in chest radiographs using a CRF-regularized FCN with local refinement

Abstract

Localization and labeling of posterior ribs in radiographs is an important task and a prerequisite for, e.g., quality assessment, image registration, and automated diagnosis. In this paper, we propose an automatic, general approach for localizing spatially correlated landmarks using a fully convolutional network (FCN) regularized by a conditional random field (CRF) and apply it to rib localization. A reduced CRF state space in form of localization hypotheses (generated by the FCN) is used to make CRF inference feasible, potentially missing correct locations. Thus, we propose a second CRF inference step searching for additional locations. To this end, we introduce a novel “refine” label in the first inference step. For “refine”-labeled nodes, small subgraphs are extracted and a second inference is performed on all image pixels. The approach is thoroughly evaluated on 642 images of the public Indiana chest X-ray collection, achieving a landmark localization rate of 94.6%.