ArXiv Preprint
Chronic Obstructive Pulmonary Disease (COPD) is a leading cause of death
worldwide, yet early detection and treatment can prevent the progression of the
disease. In contrast to the conventional method of detecting COPD with
spirometry tests, X-ray Computed Tomography (CT) scans of the chest provide a
measure of morphological changes in the lung. It has been shown that automated
detection of COPD can be performed with deep learning models. However, the
potential of incorporating optimal window setting selection, typically carried
out by clinicians during examination of CT scans for COPD, is generally
overlooked in deep learning approaches. We aim to optimize the binary
classification of COPD with densely connected convolutional neural networks
(DenseNets) through implementation of manual and automated Window-Setting
Optimization (WSO) steps. Our dataset consisted of 78 CT scans from the
Klinikum rechts der Isar research hospital. Repeated inference on the test set
showed that without WSO, the plain DenseNet resulted in a mean slice-level AUC
of 0.80$\pm$0.05. With input images manually adjusted to the emphysema window
setting, the plain DenseNet model predicted COPD with a mean AUC of
0.86$\pm$0.04. By automating the WSO through addition of a customized layer to
the DenseNet, an optimal window setting in the proximity of the emphysema
window setting was learned and a mean AUC of 0.82$\pm$0.04 was achieved.
Detection of COPD with DenseNet models was optimized by WSO of CT data to the
emphysema window setting range, demonstrating the importance of implementing
optimal window setting selection in the deep learning pipeline.
Tina Dorosti, Manuel Schultheiss, Felix Hofmann, Luisa Kirchner, Theresa Urban, Franz Pfeiffer, Johannes Thalhammer, Florian Schaff, Tobias Lasser, Daniela Pfeiffer
2023-03-13
