  In the medical images field, semantic segmentation is one of the most
important, yet difficult and time-consuming tasks to be performed by
physicians. Thanks to the recent advancement in the Deep Learning models
regarding Computer Vision, the promise to automate this kind of task is getting
more and more realistic. However, many problems are still to be solved, like
the scarce availability of data and the difficulty to extend the efficiency of
highly specialised models to general scenarios. Organs at risk segmentation for
radiotherapy treatment planning falls in this category, as the limited data
available negatively affects the possibility to develop general-purpose models;
in this work, we focus on the possibility to solve this problem by presenting
three types of ensembles of single-organ models able to produce multi-organ
masks exploiting the different specialisations of their components. The results
obtained are promising and prove that this is a possible solution to finding
efficient multi-organ segmentation methods.
