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

Basic Ensembles of Vanilla-Style Deep Learning Models Improve Liver Segmentation From CT Images