  Segmentation of the liver from 3D computer tomography (CT) images is one of
the most frequently performed operations in medical image analysis. In the past
decade, Deep Learning Models (DMs) have offered significant improvements over
previous methods for liver segmentation. The success of DMs is usually owed to
the user's expertise in deep learning as well as to intricate training
procedures. The need for bespoke expertise limits the reproducibility of
empirical studies involving DMs. Today's consensus is that an ensemble of DMs
works better than the individual component DMs. In this study we set off to
explore the potential of ensembles of publicly available, `vanilla-style' DM
segmenters Our ensembles were created from four off-the-shelf DMs: U-Net,
Deepmedic, V-Net, and Dense V-Networks. To prevent further overfitting and to
keep the overall model simple, we use basic non-trainable ensemble combiners:
majority vote, average, product and min/max. Our results with two publicly
available data sets (CHAOS and 3Dircadb1) demonstrate that ensembles are
significantly better than the individual segmenters on four widely used
metrics.
