  CNN (Convolutional Neural Network) models have been successfully used for
segmentation of the left ventricle (LV) in cardiac MRI (Magnetic Resonance
Imaging), providing clinical measurements. In practice, two questions arise
with deployment of CNNs: 1) when is it better to use a shallow model instead of
a deeper one? 2) how the size of a dataset might change the network
performance? We propose a framework to answer them, by experimenting with deep
and shallow versions of three U-Net families, trained from scratch in six
subsets varying from 100 to 10,000 images, different network sizes, learning
rates and regularization values. 1620 models were evaluated using 5-fold
cross-validation by loss and DICE. The results indicate that: sample size
affects performance more than architecture or hyper-parameters; in small
samples the performance is more sensitive to hyper-parameters than
architecture; the performance difference between shallow and deeper networks is
not the same across families.
