  Segmentation of endoscopic images is an essential processing step for
computer and robotics-assisted interventions. The Robust-MIS challenge provides
the largest dataset of annotated endoscopic images to date, with 5983 manually
annotated images. Here we describe OR-UNet, our optimized robust residual 2D
U-Net for endoscopic image segmentation. As the name implies, the network makes
use of residual connections in the encoder. It is trained with the sum of Dice
and cross-entropy loss and deep supervision. During training, extensive data
augmentation is used to increase the robustness. In an 8-fold cross-validation
on the training images, our model achieved a mean (median) Dice score of 87.41
(94.35). We use the eight models from the cross-validation as an ensemble on
the test set.
