论文标题
大规模细胞电子显微镜的分割，深度学习：文献调查
Segmentation in large-scale cellular electron microscopy with deep learning: A literature survey
论文作者
论文摘要
生物医学电子显微镜（EM）中的自动化和半自动化技术使得能够以很高的速率采集大型数据集。因此，分割方法对于分析和解释这些大量数据至关重要，这些数据不再完全被手动标记。近年来，深度学习算法在像素级标签（语义分割）和同一类的单独实例（实例分割）的标签中都取得了令人印象深刻的结果。在这篇综述中，我们研究了这些算法如何适应EM图像中细胞和亚细胞结构的任务。描述了这样的图像和克服了其中一些的网络体系结构所带来的特殊挑战。此外，在著名的数据集中还提供了详尽的概述，这些数据集有助于EM中深度学习的扩散。最后，给出了当前趋势和EM细分未来前景的前景，尤其是在无标签学习领域。
Automated and semi-automated techniques in biomedical electron microscopy (EM) enable the acquisition of large datasets at a high rate. Segmentation methods are therefore essential to analyze and interpret these large volumes of data, which can no longer completely be labeled manually. In recent years, deep learning algorithms achieved impressive results in both pixel-level labeling (semantic segmentation) and the labeling of separate instances of the same class (instance segmentation). In this review, we examine how these algorithms were adapted to the task of segmenting cellular and sub-cellular structures in EM images. The special challenges posed by such images and the network architectures that overcame some of them are described. Moreover, a thorough overview is also provided on the notable datasets that contributed to the proliferation of deep learning in EM. Finally, an outlook of current trends and future prospects of EM segmentation is given, especially in the area of label-free learning.
