  There has recently been great progress in automatic segmentation of medical
images with deep learning algorithms. In most works observer variation is
acknowledged to be a problem as it makes training data heterogeneous but so far
no attempts have been made to explicitly capture this variation. Here, we
propose an approach capable of mimicking different styles of segmentation,
which potentially can improve quality and clinical acceptance of automatic
segmentation methods. In this work, instead of training one neural network on
all available data, we train several neural networks on subgroups of data
belonging to different segmentation variations separately. Because a priori it
may be unclear what styles of segmentation exist in the data and because
different styles do not necessarily map one-on-one to different observers, the
subgroups should be automatically determined. We achieve this by searching for
the best data partition with a genetic algorithm. Therefore, each network can
learn a specific style of segmentation from grouped training data. We provide
proof of principle results for open-sourced prostate segmentation MRI data with
simulated observer variations. Our approach provides an improvement of up to
23% (depending on simulated variations) in terms of Dice and surface Dice
coefficients compared to one network trained on all data.
