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.

计算机视觉系统具有众多工具，可协助各种医学领域，尤其是图像诊断。计算机断层扫描（CT）是主要的成像方法，可用于协助诊断诸如骨折，肺癌，心脏病和肺气肿等疾病。肺癌是世界上四大死亡原因之一。CT图像中的肺区域由专家手动标记，因为此初始步骤对于计算机视觉技术而言是一项重大挑战。确定后，将肺区域进行分割以进行临床诊断。这项工作提出了使用卷积神经网络（CNN）蒙版R-CNN对CT图像中的肺部进行自动分割的方法，以将肺区域映射模型专门化，并结合有监督和无监督的机器学习方法（贝叶斯，支持向量机（ SVM），K均值和高斯混合模型（GMM））。我们将Mask R-CNN与K-means内核结合使用的方法对肺部分割产生了最佳结果，准确度达到97.68±3.42％，平均运行时间为11.2 s。为了进行验证，我们将结果与其他作品进行了比较，我们的方法具有最高的准确性，并且比某些最新方法要快。