The dataset consists of short-axis and long-axis cine CMR images of 5,008 subjects (61.2 ±7.2 years, 52.5% female), acquired from the UK Biobank. The baseline characteristics of the UK Biobank cohort can be viewed in the data showcase at [16 ]. For short-axis images, the in-plane image resolution is 1.8 ×1.8 mm2 with slice thickness of 8.0 mm and slice gap of 2 mm. A short-axis image stack typically consists of 10 image slices. For long-axis images, the in-plane image resolution is 1.8 ×1.8 mm2 and only 1 image slice is acquired. Each cardiac cycle consists of 50 time frames. For both short-axis and long-axis views, the balanced steady-state free precession (bSSFP) magnitude images were used for analysis. Details of the image acquisition protocol can be found in [17 (link)].
Manual image annotation was undertaken by a team of eight observers under the guidance of three principal investigators and following a standard operating procedure [18 (link)]. For short-axis images, the LV endocardial and epicardial borders and the RV endocardial borders were manually traced at ED and ES time frames using the cvi42 software (version 5.1.1, Circle Cardiovascular Imaging Inc., Calgary, Alberta, Canada). For long-axis 2-chamber view (2Ch) images, the left atrium (LA) endocardial border was traced. For long-axis 4-chamber view (4Ch) images, the LA and the right atrium (RA) endocardial borders were traced.
In pre-processing, the CMR DICOM images were converted into NIfTI format. The manual annotations from the cvi42 software were exported as XML files and also converted into NIfTI format. The images and annotations were quality controlled to ensure that annotations cover both ED and ES frames and without missing slices or missing anatomical structures. For short-axis images, 4,875 subjects (with 93,500 annotated image slices) were available after quality control, which were randomly split into three sets of 3,975/300/600 for training/validation/test, i.e. 3,975 subjects for training the neural network, 300 validation subjects for tuning model parameters, and finally 600 test subjects for evaluating performance. For long-axis 2Ch images, 4,723 subjects were available after quality control, which were split into 3,823/300/600. For long-axis 4Ch images, 4,682 subjects were available, which were split into 3,782/300/600.
Manual image annotation was undertaken by a team of eight observers under the guidance of three principal investigators and following a standard operating procedure [18 (link)]. For short-axis images, the LV endocardial and epicardial borders and the RV endocardial borders were manually traced at ED and ES time frames using the cvi42 software (version 5.1.1, Circle Cardiovascular Imaging Inc., Calgary, Alberta, Canada). For long-axis 2-chamber view (2Ch) images, the left atrium (LA) endocardial border was traced. For long-axis 4-chamber view (4Ch) images, the LA and the right atrium (RA) endocardial borders were traced.
In pre-processing, the CMR DICOM images were converted into NIfTI format. The manual annotations from the cvi42 software were exported as XML files and also converted into NIfTI format. The images and annotations were quality controlled to ensure that annotations cover both ED and ES frames and without missing slices or missing anatomical structures. For short-axis images, 4,875 subjects (with 93,500 annotated image slices) were available after quality control, which were randomly split into three sets of 3,975/300/600 for training/validation/test, i.e. 3,975 subjects for training the neural network, 300 validation subjects for tuning model parameters, and finally 600 test subjects for evaluating performance. For long-axis 2Ch images, 4,723 subjects were available after quality control, which were split into 3,823/300/600. For long-axis 4Ch images, 4,682 subjects were available, which were split into 3,782/300/600.