Weakly Supervised Learning Strategy for Lung Defect Segmentation

Robin Sandkühler, Christoph Jud, Grzegorz Bauman, Corin Willers, Orso Pusterla, Sylvia Nyilas, Alan Peters, Lukas Ebner, Enno Stranziger, Oliver Bieri, Philipp Latzin, Philippe C. Cattin

Abstract: Through the development of specific magnetic resonance sequences, it is possible to measure the physiological properties of the lung parenchyma, e.g., ventilation. Automatic segmentation of pathologies in such ventilation maps is essential for the clinical application. The generation of labeled ground truth data is costly, time-consuming and requires much experience in the field of lung anatomy and physiology. In this paper, we present a weakly supervised learning strategy for the segmentation of defected lung areas in those ventilation maps. As a weak label, we use the Lung Clearance Index (LCI) which is measured by a Multiple Breath Washout test. The LCI is a single global measure for the ventilation inhomogeneities of the whole lung. We designed a network and a training procedure in order to infer a pixel-wise segmentation from the global LCI value. Our network is composed of two autoencoder sub-networks for the extraction of global and local features respectively. Furthermore, we use self-supervised regularization to prevent the network from learning non-meaningful segmentations. The performance of our method is evaluated by a rating of the created defect segmentations by 5 human experts, where over $$60\%$$ of the segmentation results are rated with very good or perfect.