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.

翻译：在医学影像领域，语义分割是医生执行的最重要、最困难和最耗时的任务之一。最近计算机视觉中深度学习模型的进展，使得自动化这种任务的承诺变得越来越现实。然而，仍有许多问题需要解决，如数据的稀缺性以及如何将高度专业化的模型的效率扩展到一般情况下。放射治疗计划中的风险器官分割属于这一类，由于数据的有限性，负面影响了开发通用模型的可能性。在本文中，我们着重介绍三种类型的单器官模型集成，能够利用其组成部分的不同专业知识生成多器官掩码。所获得的结果表明，这是查找高效多器官分割方法的一个可能解决方案。