## 2 Methods  
### Cardiac MRI Datasets  
We investigated two cine cardiac MR datasets from the Left Ventricle Segmentation Challenge (LVSC) held in 2009 [20] and the 2017 Automated Cardiac Diagnosis Challenge (ACDC) [9]. The LVSC dataset ([[https://www.cardiac.msg./studies/sunnybrook-cardiac-data/](https://www.cardiac.msg./studies/sunnybrook-cardiac-data/)]([https://www.cardiac.msg./studies/sunnybrook-cardiac-data/](https://www.cardiac.msg./studies/sunnybrook-cardiac-data/)), accessed 20 March 2021) consists of 45 subjects (mean age = 61 \(\pm\) 15 years, age range = [23; 88] years; 32 male) enrolled in clinical studies at Sunnybrook Health Sciences Centre (Canada), including healthy volunteers (\(n\) = 9) and patients with hypertrophy (\(n\) = 12), or with heart failure with (\(n\) = 12) and without (\(n\) = 12) infarction. Two-dimensional short-axis cine images of the whole heart were obtained with as SSFP sequence (voxel size = 1.25-1.56 mm\({}^{2}\), slice thickness = 8-10 mm, inter-slice gap = 8 mm, 6-12 slices, 20 phases per cardiac cycle) on a 1.5T scanner (Signa, GE Healthcare, Milwaukee, WI, USA). For each subject, both the myocardium (LVM) and blood cavity (LVC) of the left ventricle in the cine images at the end-diastole were manually segmented by a cardiologist, and only the LV cavity was manually segmented at the end-systolic phase. Therefore, only the cine MRI datasets at the end-diastolic phase (\(n\) = 45 images) were used in this study.  
The ACDC dataset ([[https://www.creatis.insa-lyon.fr/Challenge/acdc/](https://www.creatis.insa-lyon.fr/Challenge/acdc/)]([https://www.creatis.insa-lyon.fr/Challenge/acdc/](https://www.creatis.insa-lyon.fr/Challenge/acdc/)), accessed 20 March 2021) comprises 100 participants (mean weight = 75 \(\pm\) 17 kg; mean height = 171 \(\pm\) 10 cm) acquired in clinical routine at the University Hospital of Dijon (France). The dataset covers five categories of well-defined pathologies (\(n\) = 20 subjects in each category): heart failure with myocardial infarction, dilated cardiomyopathy, hypertrophic cardiomyopathy, and abnormal right ventricle, as well as healthy subjects. Two-dimensional short-axis cine images covering the entire LV were acquired on 1.5T or 3.0T scanners (Siemens Aera and Siemens Trio, Siemens Medical Solutions, Germany) using an SSFP sequence (voxel size = 1.34-1.68 mm\({}^{2}\), slice thickness = 5-10 mm, inter-slice gap = 5 mm (sometimes), 6-18 slices, 28-40 phases per cardiac cycle). The dataset had substantial variability in image quality, including noise, motion and banding artefacts, MR low-frequency intensity fluctuation, and varying field-of-view. Manual segmentation of the LVM and LVC was performed on the cine images at both end-diastolic and end-systolic phases, which were double-checked by two independent experts to reach consensus.  
We note that the manual segmentation of the LVSC dataset is not very consistent between subjects and there is substantial \"noise\" in manual annotations. In addition, the LVSC dataset contains cine images with LV cavity and myocardium segmentation only at the end-diastolic phase. We used the LVSC dataset for CNN pre-training, which provides additional opportunity to explore the tolerance to annotation noise and generalizability from end-diastolic phase to end-systolic phase for a deep-learning segmentation algorithm. The ACDC dataset was randomly divided into 10 and 90 subjects for CNN fine-tuning and testing, respectively. All data used in this study were anonymized and ethics approval for using these public datasets was exempted.