Separation of target anatomical structure and occlusions in chest radiographs

• URL: http://arxiv.org/abs/2002.00751v1
• Date: Mon, 3 Feb 2020 14:01:06 GMT
• Title: Separation of target anatomical structure and occlusions in chest radiographs
• Authors: Johannes Hofmanninger, Sebastian Roehrich, Helmut Prosch and Georg Langs
• Abstract summary: We propose a Fully Convolutional Network to suppress, for a specific task, undesired visual structure from radiographs. The proposed algorithm creates reconstructed radiographs and ground-truth data from high resolution CT-scans.
• Score: 2.0478628221188497
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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