Prediction of Lung CT Scores of Systemic Sclerosis by Cascaded Regression Neural Networks

Visually scoring lung involvement in systemic sclerosis (SSc) from CT scans plays an important role in monitoring progression, but its labor intensiveness hinders practical application. We proposed, therefore, an automatic scoring framework that consists of two cascaded deep regression neural networks. The first (3D) network aims to predict the craniocaudal position of five anatomically defined scoring levels on the 3D CT scans. The second (2D) network receives the resulting 2D axial slices and predicts the scores, which represent the extent of SSc disease. CT scans from 227 patients were used for both networks. 180 scans were split into four groups with equal number of samples to perform four-fold cross validation and an additional set of 47 scans constitute a separate testing dataset. Two experts scored all CT data in consensus and to obtain inter-observer variabilities they also scored independently 16 patients from the testing dataset. To alleviate the unbalance in training labels in the second network, we introduced a balanced sampling technique and to increase the diversity of the training samples, synthetic data was generated, mimicking ground glass and reticulation patterns. The four-fold cross validation results showed that our proposed score prediction network achieved an average MAE of 5.90, 4.66 and 4.49%, weighted kappa of 0.66, 0.58 and 0.65 for total score (TOT), ground glass (GG) and reticular pattern (RET), respectively. Our network performed slightly worse than the best human observation on TOT and GG prediction but it has competitive performance on RET prediction and has the potential to be an objective alternative for the visual scoring of SSc in CT thorax studies.