Manual segmentation of stacks of 2D biomedical images (e.g., histology) is a time-consuming task which can be sped up with semi-automated techniques. In this article, we present a suggestive deep active learning framework that seeks to minimise the annotation effort required to achieve a certain level of accuracy when labelling such a stack. The framework suggests, at every iteration, a specific region of interest (ROI) in one of the images for manual delineation. Using a deep segmentation neural network and a mixed cross-entropy loss function, we propose a principled strategy to estimate class probabilities for the whole stack, conditioned on heterogeneous partial segmentations of the 2D images, as well as on weak supervision in the form of image indices that bound each ROI. Using the estimated probabilities, we propose a novel active learning criterion based on predictions for the estimated segmentation performance and delineation effort, measured with average Dice scores and total delineated boundary length, respectively, rather than common surrogates such as entropy. The query strategy suggests the ROI that is expected to maximise the ratio between performance and effort, while considering the adjacency of structures that may have already been labelled – which decrease the length of the boundary to trace. We provide quantitative results on synthetically deformed MRI scans and real histological data, showing that our framework can reduce labelling effort by up to 60-70% without compromising accuracy.

对 2D 生物医学图像（例如，组织学）堆栈进行手动分割是一项耗时的任务，但可以通过半自动化技术加快处理速度。在这篇文章中，我们提出了一个建议深度主动学习框架，旨在最大限度地减少在标记此类堆栈时达到一定准确度所需的注释工作。该框架在每次迭代时建议在其中一张图像中指定一个特定的感兴趣区域 (ROI) 以进行手动描绘。使用深度分割神经网络和混合交叉熵损失函数，我们提出了一种原则性策略来估计整个堆栈的类概率，条件是二维图像的异构部分分割，以及图像形式的弱监督约束每个 ROI 的指标。使用估计的概率，我们提出了一种新的主​​动学习标准，该标准基于对估计分割性能和描绘工作的预测，分别用平均骰子分数和总描绘边界长度来衡量，而不是常见的替代品，例如熵。查询策略建议的 ROI 有望最大化性能和工作量之间的比率，同时考虑可能已经标记的结构的邻接性——这会减少要跟踪的边界的长度。我们提供了合成变形 MRI 扫描和真实组织学数据的定量结果，表明我们的框架可以在不影响准确性的情况下减少高达 60-70% 的标记工作。