  High-fidelity semantic segmentation of magnetic resonance volumes is critical
for estimating tissue morphometry and relaxation parameters in both clinical
and research applications. While manual segmentation is accepted as the
gold-standard, recent advances in deep learning and convolutional neural
networks (CNNs) have shown promise for efficient automatic segmentation of soft
tissues. However, due to the stochastic nature of deep learning and the
multitude of hyperparameters in training networks, predicting network behavior
is challenging. In this paper, we quantify the impact of three factors
associated with CNN segmentation performance: network architecture, training
loss functions, and training data characteristics. We evaluate the impact of
these variations on the segmentation of femoral cartilage and propose potential
modifications to CNN architectures and training protocols to train these models
with confidence.
