Adaptive Level Set with region analysis via Mask R-CNN: A comparison against classical methods

The World Health Organization (WHO) registered around 3 million deaths caused by Chronic Obstructive Pulmonary Disease (COPD), representing 5% of all deaths registered in 2015. Computed tomography (CT) is among the main exam for clinical diagnosis of lung diseases. However, the first challenge experienced by the radiology specialist is to define the region of interest. Thus, the identification of diseases using systems of computer-aided diagnosis (CAD) medical via image processing techniques offers more accuracy and agility for diagnosis. In this paper, we propose a new automatic segmentation of lungs in CT images. Our method uses a deep learning technique called Mask Region-Based Convolutional Neural Network (Mask R-CNN), combined with an adaptive active contour method called Fast Morphological Geodesic Active Contour (FGAC). The proposed method was evaluated with 72 lung images, consisting of 24 images of healthy volunteers and 48 of unhealthy patients. Our approach achieved promising results with Accuracy of 98.93%, Matthews Correlation Coefficient of 95.84%, Hausdorff Distance of 5.48, DICE of 96.47%, and Jaccard of 93.24%. Thus, our method surpasses a recent classic approach that also uses FGAC as a segmentation method.