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精确的手动标注的可用性。 然而，生成这些标注通常耗时、昂贵，并且有时需要专门的知识（尤其是对于横断面医学图像）。 因此，优化标注资源的使用以确保效率和效果是至关重要的。 在本文中，我们系统地解决了以下问题：“在非交互式标注流程中，如何选择横断面医学图像的切片进行标注，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，涉及不同的标注预算、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总是更优的。 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）选择切片进行标注并不优于随机选择或固定间隔选择。 3）在标注切片之间插值掩码很少能提高模型性能，除非是某些特定配置的3D模型。 显示英文摘要

摘要： Automated segmentation of medical images heavily relies on the availability of precise manual annotations. However, generating these annotations is often time-consuming, expensive, and sometimes requires specialized expertise (especially for cross-sectional medical images). Therefore, it is essential to optimize the use of annotation resources to ensure efficiency and effectiveness. In this paper, we systematically address the question: "in a non-interactive annotation pipeline, how should slices from cross-sectional medical images be selected for annotation to maximize the 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.