  Deep learning has led to state-of-the-art results for many medical imaging
tasks, such as segmentation of different anatomical structures. With the
increased numbers of deep learning publications and openly available code, the
approach to choosing a model for a new task becomes more complicated, while
time and (computational) resources are limited. A possible solution to choosing
a model efficiently is meta-learning, a learning method in which prior
performance of a model is used to predict the performance for new tasks. We
investigate meta-learning for segmentation across ten datasets of different
organs and modalities. We propose four ways to represent each dataset by
meta-features: one based on statistical features of the images and three are
based on deep learning features. We use support vector regression and deep
neural networks to learn the relationship between the meta-features and prior
model performance. On three external test datasets these methods give Dice
scores within 0.10 of the true performance. These results demonstrate the
potential of meta-learning in medical imaging.
