Fully\u2011automated deep\u2011learning segmentation of pediatric cardiovascular magnetic resonance of patients with complex congenital heart diseases

Abstract

BackgroundFor the growing patient population with congenital heart disease (CHD), improving clinical workflow, accuracy of diagnosis, and efficiency of analyses are considered unmet clinical needs. Cardiovascular magnetic resonance (CMR) imaging offers non-invasive and non-ionizing assessment of CHD patients. However, although CMR data facilitates reliable analysis of cardiac function and anatomy, clinical workflow mostly relies on manual analysis of CMR images, which is time consuming. Thus, an automated and accurate segmentation platform exclusively dedicated to pediatric CMR images can significantly improve the clinical workflow, as the present work aims to establish.MethodsTraining artificial intelligence (AI) algorithms for CMR analysis requires large annotated datasets, which are not readily available for pediatric subjects and particularly in CHD patients. To mitigate this issue, we devised a novel method that uses a generative adversarial network (GAN) to synthetically augment the training dataset via generating synthetic CMR images and their corresponding chamber segmentations. In addition, we trained and validated a deep fully convolutional network (FCN) on a dataset, consisting of 64 pediatric subjects with complex CHD, which we made publicly available. Dice metric, Jaccard index and Hausdorff distance as well as clinically-relevant volumetric indices are reported to assess and compare our platform with other algorithms including U-Net and cvi42, which is used in clinics.ResultsFor congenital CMR dataset, our FCN model yields an average Dice metric of 91.0mathrm{%} and 86.8mathrm{%} for LV at end-diastole and end-systole, respectively, and 84.7mathrm{%} and 80.6mathrm{%} for RV at end-diastole and end-systole, respectively. Using the same dataset, the cvi42, resulted in 73.2mathrm{%}, 71.0mathrm{%}, 54.3mathrm{%} and 53.7mathrm{%} for LV and RV at end-diastole and end-systole, and the U-Net architecture resulted in 87.4mathrm{%}, 83.9mathrm{%}, 81.8mathrm{%} and 74.8mathrm{%} for LV and RV at end-diastole and end-systole, respectively.ConclusionsThe chambers’ segmentation results from our fully-automated method showed strong agreement with manual segmentation and no significant statistical difference was found by two independent statistical analyses. Whereas cvi42 and U-Net segmentation results failed to pass the t-test. Relying on these outcomes, it can be inferred that by taking advantage of GANs, our method is clinically relevant and can be used for pediatric and congenital CMR segmentation and analysis.