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2 March 2018 Quantification of lung abnormalities in cystic fibrosis using deep networks
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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 .
Conference Presentation
© (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Filipe Marques, Florian Dubost, Mariette Kemner-van de Corput, Harm A. W. Tiddens, and Marleen de Bruijne "Quantification of lung abnormalities in cystic fibrosis using deep networks", Proc. SPIE 10574, Medical Imaging 2018: Image Processing, 105741G (2 March 2018);

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