  Magnetic resonance (MR) imaging is an essential diagnostic tool in clinical
medicine. Recently, a variety of deep learning methods have been applied to
segmentation tasks in medical images, with promising results for computer-aided
diagnosis. For MR images, effectively integrating different pulse sequences is
important to optimize performance. However, the best way to integrate different
pulse sequences remains unclear. In this study, we evaluate multiple
architectural features and characterize their effects in the task of metastasis
segmentation. Specifically, we consider (1) different pulse sequence
integration schemas, (2) different modes of weight sharing for parallel network
branches, and (3) a new approach for enabling robustness to missing pulse
sequences. We find that levels of integration and modes of weight sharing that
favor low variance work best in our regime of small data (n = 100). By adding
an input-level dropout layer, we could preserve the overall performance of
these networks while allowing for inference on inputs with missing pulse
sequence. We illustrate not only the generalizability of the network but also
the utility of this robustness when applying the trained model to data from a
different center, which does not use the same pulse sequences. Finally, we
apply network visualization methods to better understand which input features
are most important for network performance. Together, these results provide a
framework for building networks with enhanced robustness to missing data while
maintaining comparable performance in medical imaging applications.
