TY - JOUR T1 - Ventilation defect quantification on 3He MRI through deep learning: the MESA COPD Study JF - European Respiratory Journal JO - Eur Respir J DO - 10.1183/13993003.congress-2020.4332 VL - 56 IS - suppl 64 SP - 4332 AU - Xuzhe Zhang AU - Elsa Angelini AU - Andrew Laine AU - Yanping Sun AU - Grant Hiura AU - Stephen Dashnaw AU - Martin Prince AU - Eric Hoffman AU - Bharath Ambale-Venkatesh AU - Joao Lima AU - Jim Wild AU - Emlyn Hughes AU - R. Graham Barr AU - Wei Shen Y1 - 2020/09/07 UR - http://erj.ersjournals.com/content/56/suppl_64/4332.abstract N2 - Purpose: Hyperpolarized helium (3He) MRI is used to assess ventilation defects in COPD and other pulmonary diseases. Existing segmentation methods are very operator-dependent and effort consuming. We aimed to develop a deep-learning based method to accelerate segmentation, eliminate operator dependence and improve reliability.Methods and Materials: The MESA COPD Study is a nested case-control study of COPD (post-bronchodilator FEV1/FVC ratio<0.70) among 10+ pack-year smokers. 56 participants (74±8 yrs) underwent same breath-hold 1H and 3He MRI. Using semi-automatic region growing techniques, labels of total lung were created on 543 1H slices and labels of ventilation defects were created on 543 3He slices. 42 randomly selected subjects were used for training, with the remaining 14 subjects used for validation. Two separate convolutional neural networks were trained to segment 1H MRI and 3He MRI with conventional data augmentation. Total lung masks via 1H MRI were imported into a second neural network to define lung boundaries on 3He MRI.Results: The percentage of non-, low-, and normal- ventilated regions were 12±9%, 25±14%, and 62±20%, respectively. At patient level, the dice coefficient scores were not significantly different between participants with and without COPD (p=0.29-0.95, Table).Conclusion: The proposed deep learning method yields accurate, automated segmentation of ventilation defects among older smokers, independent of COPD status.Funding NIH/NHLBI R01-HL093081, R01-HL077612, R01-HL121270View this table:FootnotesCite this article as: European Respiratory Journal 2020; 56: Suppl. 64, 4332.This abstract was presented at the 2020 ERS International Congress, in session “Respiratory viruses in the "pre COVID-19" era”.This is an ERS International Congress abstract. No full-text version is available. Further material to accompany this abstract may be available at www.ers-education.org (ERS member access only). ER -