Quantification of Lung Abnormalities in Cystic Fibrosis using Deep Networks
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Quantification of Lung Abnormalities in Cystic Fibrosis using Deep Networks. / Marques, Filipe; Dubost, Florian; Corput, Mariette Kemner-van de; Tiddens, Harm A. W.; Bruijne, Marleen de.
Medical Imaging 2018: Image Processing. SPIE - International Society for Optical Engineering, 2018. 105741G (Proceedings of SPIE International Symposium on Medical Imaging, Bind 10574).Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
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TY - GEN
T1 - Quantification of Lung Abnormalities in Cystic Fibrosis using Deep Networks
AU - Marques, Filipe
AU - Dubost, Florian
AU - Corput, Mariette Kemner-van de
AU - Tiddens, Harm A. W.
AU - Bruijne, Marleen de
N1 - SPIE - Medical Imaging 2018: Image Processing
PY - 2018
Y1 - 2018
N2 - 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 an accuracy of 0,94 for disease detection, 0,18 higher than the random forest classifier and 0,37 higher than the single neural network. Our cascade approach yields a final class-averaged F1-score of 0,33, outperforming the baseline method and the single network by 0,10 and 0,12.
AB - 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 an accuracy of 0,94 for disease detection, 0,18 higher than the random forest classifier and 0,37 higher than the single neural network. Our cascade approach yields a final class-averaged F1-score of 0,33, outperforming the baseline method and the single network by 0,10 and 0,12.
KW - cs.CV
U2 - 10.1117/12.2292188
DO - 10.1117/12.2292188
M3 - Article in proceedings
T3 - Proceedings of SPIE International Symposium on Medical Imaging
BT - Medical Imaging 2018
PB - SPIE - International Society for Optical Engineering
T2 - SPIE Medical Imaging 2018
Y2 - 10 February 2018 through 15 February 2018
ER -
ID: 202482614