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Abstract #3226

Simultaneous segmentation of airways and ventilated lung in hyperpolarised-gas MR images by deep learning

Fabien J Bertin1, Guilhem J Collier2, Paul JC Hughes2, Laurie Smith2, James Aeden2, Helen Marshall2, Jim M Wild2,3, and Alberto M Biancardi2
1Télécom SudParis, Paris, France, 2POLARIS, Department of Infection, Immunity and Cardiovascular Disease, The University of Sheffield, Sheffield, United Kingdom, 3Insigneo Institute for in silico Medicine, The University of Sheffield, Sheffield, United Kingdom

The assessment of pulmonary hyperpolarised(HP)-gas MR images is instrumental in identifying potential pathologies, directing treatment, or monitoring disease progression. HP-gas images quantify the amount of gas concentration, whose distribution within the lungs is analysed. A key role in this process is played the main airways that are identified for quality control and excluded for the analysis. Currently, this task is performed manually and existing deep-learning (DL) applications do not provide an explicit labelling of the airways. A specific tailoring of a well-known DL approach was developed with the aim of replacing the manual editing thanks to its good performance.

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