  Domain shift refers to the difference in the data distribution of two
datasets, normally between the training set and the test set for machine
learning algorithms. Domain shift is a serious problem for generalization of
machine learning models and it is well-established that a domain shift between
the training and test sets may cause a drastic drop in the model's performance.
In medical imaging, there can be many sources of domain shift such as different
scanners or scan protocols, different pathologies in the patient population,
anatomical differences in the patient population (e.g. men vs women) etc.
Therefore, in order to train models that have good generalization performance,
it is important to be aware of the domain shift problem, its potential causes
and to devise ways to address it. In this paper, we study the effect of domain
shift on left and right ventricle blood pool segmentation in short axis cardiac
MR images. Our dataset contains short axis images from 4 different MR scanners
and 3 different pathology groups. The training is performed with nnUNet. The
results show that scanner differences cause a greater drop in performance
compared to changing the pathology group, and that the impact of domain shift
is greater on right ventricle segmentation compared to left ventricle
segmentation. Increasing the number of training subjects increased
cross-scanner performance more than in-scanner performance at small training
set sizes, but this difference in improvement decreased with larger training
set sizes. Training models using data from multiple scanners improved
cross-domain performance.
