标题： 如何选择用于标注的切片以训练在横断面医学图像上表现最佳的深度学习分割模型？ 显示英文标题

标题： How to select slices for annotation to train best-performing deep learning segmentation models for cross-sectional medical images?

摘要： 医学图像的自动分割高度依赖于准确的手动图像标注的可用性。 这样的标注生成非常耗时且成本高昂，通常需要专门的专业知识，尤其是对于横断面图像，每个患者包含许多切片。 确保最佳利用标注资源至关重要。 在本文中，我们系统地回答了如何选择横断面医学图像的切片，以最大化所得深度学习分割模型的性能。 我们在4个医学影像分割任务上进行了实验，这些任务具有不同的标注预算、标注病例数量、每体积的标注切片数量、切片选择技术和掩码插值。 我们发现： 1) 在给定标注预算的情况下，标注更少的切片和更多体积几乎是优选的。 2) 在每个体积分配相同数量的标注切片的前提下，通过无监督主动学习（UAL）选择切片并不优于随机选择或固定间隔选择切片。 3) 在标注切片之间插值掩码很少能提高模型性能，除了某些特定配置的3D模型。 显示英文摘要

摘要： Automated segmentation of medical images highly depends on the availability of accurate manual image annotations. Such annotations are very time-consuming and costly to generate, and often require specialized expertise, particularly for cross-sectional images which contain many slices for each patient. It is crucial to ensure the best use of annotation resources. In this paper, we systematically answer the question of how to select slices of cross-sectional medical images in order to maximize performance of the resulting deep learning segmentation models. We conducted experiments on 4 medical imaging segmentation tasks with varying annotation budgets, numbers of annotated cases, numbers of annotated slices per volume, slice selection techniques, and mask interpolations. We found that: 1) It is almost always preferable to annotate fewer slices per volume and more volumes given an annotation budget. 2) Selecting slices for annotation by unsupervised active learning (UAL) is not superior to selecting slices randomly or at fixed intervals, provided that each volume is allocated the same number of annotated slices. 3) Interpolating masks between annotated slices rarely enhances model performance, with exceptions of some specific configuration for 3D models.