  Brain lesion and anatomy segmentation in magnetic resonance images are
fundamental tasks in neuroimaging research and clinical practice. Given enough
training data, convolutional neuronal networks (CNN) proved to outperform all
existent techniques in both tasks independently. However, to date, little work
has been done regarding simultaneous learning of brain lesion and anatomy
segmentation from disjoint datasets.
  In this work we focus on training a single CNN model to predict brain tissue
and lesion segmentations using heterogeneous datasets labeled independently,
according to only one of these tasks (a common scenario when using publicly
available datasets). We show that label contradiction issues can arise in this
case, and propose a novel adaptive cross entropy (ACE) loss function that makes
such training possible. We provide quantitative evaluation in two different
scenarios, benchmarking the proposed method in comparison with a multi-network
approach. Our experiments suggest that ACE loss enables training of single
models when standard cross entropy and Dice loss functions tend to fail.
Moreover, we show that it is possible to achieve competitive results when
comparing with multiple networks trained for independent tasks.
