  Planning of radiotherapy involves accurate segmentation of a large number of
organs at risk, i.e. organs for which irradiation doses should be minimized to
avoid important side effects of the therapy. We propose a deep learning method
for segmentation of organs at risk inside the brain region, from Magnetic
Resonance (MR) images. Our system performs segmentation of eight structures:
eye, lens, optic nerve, optic chiasm, pituitary gland, hippocampus, brainstem
and brain. We propose an efficient algorithm to train neural networks for an
end-to-end segmentation of multiple and non-exclusive classes, addressing
problems related to computational costs and missing ground truth segmentations
for a subset of classes. We enforce anatomical consistency of the result in a
postprocessing step, in particular we introduce a graph-based algorithm for
segmentation of the optic nerves, enforcing the connectivity between the eyes
and the optic chiasm. We report cross-validated quantitative results on a
database of 44 contrast-enhanced T1-weighted MRIs with provided segmentations
of the considered organs at risk, which were originally used for radiotherapy
planning. In addition, the segmentations produced by our model on an
independent test set of 50 MRIs are evaluated by an experienced radiotherapist
in order to qualitatively assess their accuracy. The mean distances between
produced segmentations and the ground truth ranged from 0.1 mm to 0.7 mm across
different organs. A vast majority (96 %) of the produced segmentations were
found acceptable for radiotherapy planning.
