  Automated medical image segmentation plays a key role in quantitative
research and diagnostics. Convolutional neural networks based on the U-Net
architecture are the state-of-the-art. A key disadvantage is the hard-coding of
the receptive field size, which requires architecture optimization for each
segmentation task. Furthermore, increasing the receptive field results in an
increasing number of weights. Recently, Neural Ordinary Differential Equations
(NODE) have been proposed, a new type of continuous depth deep neural network.
This framework allows for a dynamic receptive field at a fixed memory cost and
a smaller amount of parameters. We show on a colon gland segmentation dataset
(GlaS) that these NODEs can be used within the U-Net framework to improve
segmentation results while reducing memory load and parameter counts.
