A deep learning pipeline for automatic analysis of multi-scan cardiovascular magnetic resonance

Journal of Cardiovascular Magnetic Resonance

Derek J. Hausenloy¹  A. Mark Richards²  Hakim Fadil³  Stephanie Marchesseau³  John J. Totman³  Mark Y. Chan⁴  Adrian Fatt-Hoe Low⁵  Hee-Hwa Ho⁶  Prabath Joseph⁶ 

[1] Cardiovascular & Metabolic Disorders Program, Duke-National University of Singapore Medical School;Cardiovascular Research Institute, National University of Singapore;Centre for Translational MR Research (TMR), National University of Singapore;Department of Medicine, Yong Loo Lin SoM, National University of Singapore;National University Heart Centre;Tan Tock Seng Hospital;

关键词: T1 mapping;    T2 mapping;    Cine short-axis;    Aortic flow;    Deep learning;    Segmentation;   

DOI  :  10.1186/s12968-020-00695-z

来源: DOAJ

【 摘 要 】

Abstract Background Cardiovascular magnetic resonance (CMR) sequences are commonly used to obtain a complete description of the function and structure of the heart, provided that accurate measurements are extracted from images. New methods of extraction of information are being developed, among them, deep neural networks are powerful tools that showed the ability to perform fast and accurate segmentation. Iq1n order to reduce the time spent by reading physicians to process data and minimize intra- and inter-observer variability, we propose a fully automatic multi-scan CMR image analysis pipeline. Methods Sequence specific U-Net 2D models were trained to perform the segmentation of the left ventricle (LV), right ventricle (RV) and aorta in cine short-axis, late gadolinium enhancement (LGE), native T1 map, post-contrast T1, native T2 map and aortic flow sequences depending on the need. The models were trained and tested on a set of data manually segmented by experts using semi-automatic and manual tools. A set of parameters were computed from the resulting segmentations such as the left ventricular and right ventricular ejection fraction (EF), LGE scar percentage, the mean T1, T1 post, T2 values within the myocardium, and aortic flow. The Dice similarity coefficient, Hausdorff distance, mean surface distance, and Pearson correlation coefficient R were used to assess and compare the results of the U-Net based pipeline with intra-observer variability. Additionally, the pipeline was validated on two clinical studies. Results The sequence specific U-Net 2D models trained achieved fast (≤ 0.2 s/image on GPU) and precise segmentation over all the targeted region of interest with high Dice scores (= 0.91 for LV, = 0.92 for RV, = 0.93 for Aorta in average) comparable to intra-observer Dice scores (= 0.86 for LV, = 0.87 for RV, = 0.95 for aorta flow in average). The automatically and manually computed parameters were highly correlated (R = 0.91 in average) showing results superior to the intra-observer variability (R = 0.85 in average) for every sequence presented here. Conclusion The proposed pipeline allows for fast and robust analysis of large CMR studies while guaranteeing reproducibility, hence potentially improving patient’s diagnosis as well as clinical studies outcome.

【 授权许可】

Unknown