Increased organ at risk segmentation accuracy is required to reduce cost and complications for patients receiving radiotherapy treatment. Some deep learning methods for the segmentation of organs at risk use a two stage process where a localisation network first crops an image to the relevant region and then a locally specialised network segments the cropped organ of interest. We investigate the accuracy improvements brought about by such a localisation stage by comparing to a single-stage baseline network trained on full resolution images. We find that localisation approaches can improve both training time and stability and a two stage process involving both a localisation and organ segmentation network provides a significant increase in segmentation accuracy for the spleen, pancreas and heart from the Medical Segmentation Decathlon dataset. We also observe increased benefits of localisation for smaller organs. Source code that recreates the main results is available at \href{https://github.com/Abe404/localise_to_segment}{this https URL}.

翻译：提高危及器官分割精度对于减少接受放疗患者的治疗成本和并发症至关重要。部分深度学习方法采用两阶段流程进行危及器官分割：先通过定位网络将图像裁剪至相关区域，再由局部专用网络对裁剪后的目标器官进行分割。我们通过将此类定位阶段与基于全分辨率图像训练的单阶段基线网络进行对比，探究其带来的精度提升。研究发现，定位方法能够优化训练时间与稳定性，且包含定位与器官分割网络的两阶段流程显著提升了医学分割十项全能数据集中脾脏、胰腺和心脏的分割精度。我们还观察到定位对较小器官的增益更为显著。复现主要结果的源代码已发布于\href{https://github.com/Abe404/localise_to_segment}{此https链接}。