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，语义分割是医师执行的最重要但仍困难且耗时的任务之一。得益于深度学习模型在计算机视觉领域的最新进展，自动化此类任务的愿景正日益接近现实。然而，仍有许多问题有待解决，例如数据稀缺以及高专业化模型难以将其效力扩展至通用场景。放射治疗计划中的危及器官分割便属于此类问题——有限的数据可用性对开发通用模型产生了负面影响。本研究聚焦于解决该问题的可能性，提出了三种单器官模型集成方法，通过利用各组件模型的不同专长来生成多器官掩膜。所获结果具有前景，证明该方法有望成为实现高效多器官分割的有效解决方案。