Pakzad, Ashkan;
Xu, Mou-Cheng;
Cheung, Wing Keung;
Vermant, Marie;
Goos, Tinne;
Sadeleer, Laurens J De;
Verleden, Stijn E;
... Jacob, Joseph; + view all
(2022)
Airway Measurement by Refinement of Synthetic Images Improves Mortality Prediction in Idiopathic Pulmonary Fibrosis.
In:
DGM4MICCAI 2022: Deep Generative Models.
(pp. pp. 106-116).
Springer Nature: Switzerland.
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Abstract
Several chronic lung diseases, like idiopathic pulmonary fibrosis (IPF) are characterised by abnormal dilatation of the airways. Quantification of airway features on computed tomography (CT) can help characterise disease progression. Physics based airway measurement algorithms have been developed, but have met with limited success in part due to the sheer diversity of airway morphology seen in clinical practice. Supervised learning methods are also not feasible due to the high cost of obtaining precise airway annotations. We propose synthesising airways by style transfer using perceptual losses to train our model, Airway Transfer Network (ATN). We compare our ATN model with a state-of-the-art GAN-based network (simGAN) using a) qualitative assessment; b) assessment of the ability of ATN and simGAN based CT airway metrics to predict mortality in a population of 113 patients with IPF. ATN was shown to be quicker and easier to train than simGAN. ATN-based airway measurements were also found to be consistently stronger predictors of mortality than simGAN-derived airway metrics on IPF CTs. Airway synthesis by a transformation network that refines synthetic data using perceptual losses is a realistic alternative to GAN-based methods for clinical CT analyses of idiopathic pulmonary fibrosis. Our source code can be found at https://github.com/ashkanpakzad/ATN that is compatible with the existing open-source airway analysis framework, AirQuant.
| Type: | Proceedings paper |
|---|---|
| Title: | Airway Measurement by Refinement of Synthetic Images Improves Mortality Prediction in Idiopathic Pulmonary Fibrosis |
| Event: | MICCAI Workshop on Deep Generative Models 2022, 22 September 2022, Singapore. |
| ISBN-13: | 9783031185755 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1007/978-3-031-18576-2_11 |
| Publisher version: | https://link.springer.com/chapter/10.1007/978-3-03... |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For the purpose of open access, the author has applied a CC-BY public copyright licence to any author accepted manuscript version arising from this submission. |
| Keywords: | Generative model evaluation, Style transfer, Computed tomography, Airway measurement, Bronchiectasis, Idiopathic pulmonary fibrosis |
| UCL classification: | UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Chemical Engineering UCL > Provost and Vice Provost Offices > UCL BEAMS UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Medicine > Respiratory Medicine UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Medicine |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10157189 |
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