  Despite the success of convolutional neural networks for 3D medical-image
segmentation, the architectures currently used are still not robust enough to
the protocols of different scanners, and the variety of image properties they
produce. Moreover, access to large-scale datasets with annotated regions of
interest is scarce, and obtaining good results is thus difficult. To overcome
these challenges, we introduce IB-U-Nets, a novel architecture with inductive
bias, inspired by the visual processing in vertebrates. With the 3D U-Net as
the base, we add two 3D residual components to the second encoder blocks. They
provide an inductive bias, helping U-Nets to segment anatomical structures from
3D images with increased robustness and accuracy. We compared IB-U-Nets with
state-of-the-art 3D U-Nets on multiple modalities and organs, such as the
prostate and spleen, using the same training and testing pipeline, including
data processing, augmentation and cross-validation. Our results demonstrate the
superior robustness and accuracy of IB-U-Nets, especially on small datasets, as
is typically the case in medical-image analysis. IB-U-Nets source code and
models are publicly available.
