Quantification of lung abnormalities in cystic fibrosis using deep networks
Cystic fibrosis is a genetic disease which may appear in early life with structural abnormalities in lung tissues. We propose to detect these abnormalities using a texture classification approach. Our method is a cascade of two convolutional neural networks. The first network detects the presence of abnormal tissues. The second network identifies the type of the structural abnormalities: bronchiectasis, atelectasis or mucus plugging.We also propose a network computing pixel-wise heatmaps of abnormality presence learning only from the patch-wise annotations. Our database consists of CT scans of 194 subjects. We use 154 subjects to train our algorithms and the 40 remaining ones as a test set. We compare our method with random forest and a single neural network approach. The first network reaches a sensitivity of 0,62 for disease detection, 0,10 higher than the random forest classifier and 0,17 higher than the single neural network. Our cascade approach yields a final class-averaged F1-score of 0,38, outperforming the baseline method and the single network by 0,15 and 0,10.
|, , , ,|
|Medical Imaging 2018: Image Processing|
|Organisation||Erasmus MC: University Medical Center Rotterdam|
Marques, F. (Filipe), Dubost, F. (Florian), van de Corput, M.P.C, Tiddens, H.A.W.M, & de Bruijne, M. (2018). Quantification of lung abnormalities in cystic fibrosis using deep networks. In Progress in Biomedical Optics and Imaging - Proceedings of SPIE. doi:10.1117/12.2292188