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Erschienen in:
2018 | OriginalPaper | Buchkapitel
Localization and Labeling of Posterior Ribs in Chest Radiographs Using a CRF-regularized FCN with Local Refinement
verfasst von : Alexander Oliver Mader, Jens von Berg, Alexander Fabritz, Cristian Lorenz, Carsten Meyer
Erschienen in: Medical Image Computing and Computer Assisted Intervention – MICCAI 2018
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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%.