Separation of target anatomical structure and occlusions in chest radiographs

2020/02/03 by Hofmanninger, Johannes, Roehrich, Sebastian, Prosch, Helmut +1
#Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #FOS: Physical sciences #I.4.3 #I.5 #Image and Video Processing (eess.IV) #J.3 #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Medical Physics (physics.med-ph) #electronic engineering #information engineering

paper · doi:10.48550/arxiv.2002.00751

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.

Related

• SCAN: Structure Correcting Adversarial Network for Organ Segmentation in Chest X-rays
• ChestNet: A Deep Neural Network for Classification of Thoracic Diseases on Chest Radiography
• Fully Convolutional Architectures for Multi-Class Segmentation in Chest Radiographs
• Deep chest X‐ray : Detection and classification of lesions based on deep convolutional neural networks
• When does Bone Suppression and Lung Field Segmentation Improve Chest X-Ray Disease Classification?