Segmentation of Lung Tomographic Images Using U-Net Deep Neural Networks

Deep Neural Networks (DNNs) are among the best methods of Artificial Intelligence, especially in computer vision, where convolutional neural networks play an important role. There are numerous architectures of DNNs, but for image processing, U-Net offers great performance in digital processing tasks such as segmentation of organs, tumors, and cells for supporting medical diagnoses. In the present work, an assessment of U-Net models is proposed, for the segmentation of computed tomography of the lung, aiming at comparing networks with different parameters. In this study, the models scored 96% Dice Similarity Coefficient on average, corroborating the high accuracy of the U-Net for segmentation of tomographic images

Radiomics of Lung Nodules: A Multi-Institutional Study of Robustness and Agreement of Quantitative Imaging Features.

Radiomics is to provide quantitative descriptors of normal and abnormal tissues during classification and prediction tasks in radiology and oncology. Quantitative Imaging Network members are developing radiomic "feature" sets to characterize tumors, in general, the size, shape, texture, intensity, margin, and other aspects of the imaging features of nodules and lesions. Efforts are ongoing for developing an ontology to describe radiomic features for lung nodules, with the main classes consisting of size, local and global shape descriptors, margin, intensity, and texture-based features, which are based on wavelets, Laplacian of Gaussians, Law's features, gray-level co-occurrence matrices, and run-length features. The purpose of this study is to investigate the sensitivity of quantitative descriptors of pulmonary nodules to segmentations and to illustrate comparisons across different feature types and features computed by different implementations of feature extraction algorithms. We calculated the concordance correlation coefficients of the features as a measure of their stability with the underlying segmentation; 68% of the 830 features in this study had a concordance CC of ≥0.75. Pairwise correlation coefficients between pairs of features were used to uncover associations between features, particularly as measured by different participants. A graphical model approach was used to enumerate the number of uncorrelated feature groups at given thresholds of correlation. At a threshold of 0.75 and 0.95, there were 75 and 246 subgroups, respectively, providing a measure for the features' redundancy