An effective approach for CT lung segmentation using mask region-based convolutional neural networks

Computer vision systems have numerous tools to assist in various medical fields, notably in image diagnosis. Computed tomography (CT) is the principal imaging method used to assist in the diagnosis of diseases such as bone fractures, lung cancer, heart disease, and emphysema, among others. Lung cancer is one of the four main causes of death in the world. The lung regions in the CT images are marked manually by a specialist as this initial step is a significant challenge for computer vision techniques. Once defined, the lung regions are segmented for clinical diagnoses. This work proposes an automatic segmentation of the lungs in CT images, using the Convolutional Neural Network (CNN) Mask R-CNN, to specialize the model for lung region mapping, combined with supervised and unsupervised machine learning methods (Bayes, Support Vectors Machine (SVM), K-means and Gaussian Mixture Models (GMMs)). Our approach using Mask R-CNN with the K-means kernel produced the best results for lung segmentation reaching an accuracy of 97.68 ± 3.42% and an average runtime of 11.2 s. We compared our results against other works for validation purposes, and our approach had the highest accuracy and was faster than some state-of-the-art methods.