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arXiv · 2002.00751

Separation of target anatomical structure and occlusions in chest radiographs

Johannes Hofmanninger·Sebastian Roehrich·Helmut Prosch·Georg Langs
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Abstract

Chest radiographs are commonly performed low-cost exams for screening and diagnosis. However, radiographs are 2D representations of 3D structures causing considerable clutter impeding visual inspection and automated image analysis. Here, we propose a Fully Convolutional Network to suppress, for a specific task, undesired visual structure from radiographs while retaining the relevant image information such as lung-parenchyma. The proposed algorithm creates reconstructed radiographs and ground-truth data from high resolution CT-scans. Results show that removing visual variation that is irrelevant for a classification task improves the performance of a classifier when only limited training data are available. This is particularly relevant because a low number of ground-truth cases is common in medical imaging.

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Johannes Hofmanninger, Sebastian Roehrich, Helmut Prosch, Georg Langs. 2020-02-03. Separation of target anatomical structure and occlusions in chest radiographs. https://arxiv.org/abs/2002.00751

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