Deep learning for screening of interstitial lung disease patterns in high-resolution CT images

AIM: To develop a screening tool for the detection of interstitial lung disease (ILD) patterns using a deep-learning method. MATERIALS AND METHODS: A fully convolutional network was used for semantic segmentation of several ILD patterns. Improved segmentation of ILD patterns was achieved using multi-scale feature extraction. Dilated convolution was used to maintain the resolution of feature maps and to enlarge the receptive field. The proposed method was evaluated on a publicly available ILD database (MedGIFT) and a private clinical research database. Several metrics, such as success rate, sensitivity, and false positives per section were used for quantitative evaluation of the proposed method. RESULTS: Sections with fibrosis and emphysema were detected with a similar success rate and sensitivity for both databases but the performance of detection was lower for consolidation compared to fibrosis and emphysema. CONCLUSION: Automatic identification of ILD patterns in a high-resolution computed tomography (CT) image was implemented using a deep-learning framework. Creation of a pretrained model with natural images and subsequent transfer learning using a particular database gives acceptable results. (C) 2020 The Royal College of Radiologists. Published by Elsevier Ltd. All rights reserved.