Segmentation in large-scale cellular electron microscopy with deep learning: A literature survey

TL;DR

This literature survey reviews how deep learning techniques have been adapted for cellular electron microscopy segmentation, highlighting challenges, architectures, datasets, and future trends like label-free learning.

Contribution

It provides a comprehensive overview of deep learning applications in EM segmentation, including architectures, datasets, challenges, and future directions.

Findings

Deep learning has significantly advanced EM segmentation accuracy.

Various network architectures have been tailored for EM data.

Emerging trends include label-free learning approaches.

Abstract

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.