Segmentation in large-scale cellular electron microscopy with deep learning: A literature survey
It reviews incremental advancements in segmentation methods for biomedical researchers dealing with large-scale electron microscopy data.
This literature survey examines how deep learning algorithms have been adapted for segmenting cellular and sub-cellular structures in electron microscopy images, addressing the challenge of analyzing large datasets that cannot be manually labeled, and provides an overview of notable datasets and future trends like label-free learning.
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.