End-to-End Supervised Lung Lobe Segmentation

Authors: Ferreira, F.T., Sousa, P., Galdran, A., Sousa, M.R. and Campilho, A.

Journal: Proceedings of the International Joint Conference on Neural Networks

Volume: 2018-July

ISBN: 9781509060146

DOI: 10.1109/IJCNN.2018.8489677

Abstract:

The segmentation and characterization of the lung lobes are important tasks for Computer Aided Diagnosis (CAD) systems related to pulmonary disease. The detection of the fissures that divide the lung lobes is non-trivial when using classical methods that rely on anatomical information like the localization of the airways and vessels. This work presents a fully automatic and supervised approach to the problem of the segmentation of the five pulmonary lobes from a chest Computer Tomography (CT) scan using a Fully RegularizedV-Net (FRV- Net), a 3D Fully Convolutional Neural Network trained end-to- end. Our network was trained and tested in a custom dataset that we make publicly available. It can correctly separate the lobes even in cases when the fissure is not well delineated, achieving 0.93 in per-lobe Dice Coefficient and 0.85 in the inter-lobar Dice Coefficient in the test set. Both quantitative and qualitative results show that the proposed method can learn to produce correct lobe segmentations even when trained on a reduced dataset.

Source: Scopus