The prospect of neural reconstruction from Electron Microscopy (EM) images
has been elucidated by the automatic segmentation algorithms. Although
segmentation algorithms eliminate the necessity of tracing the neurons by hand,
significant manual effort is still essential for correcting the mistakes they
make. A considerable amount of human labor is also required for annotating
groundtruth volumes for training the classifiers of a segmentation framework.
It is critically important to diminish the dependence on human interaction in
the overall reconstruction system. This study proposes a novel classifier
training algorithm for EM segmentation aimed to reduce the amount of manual
effort demanded by the groundtruth annotation and error refinement tasks.
Instead of using an exhaustive pixel level groundtruth, an active learning
algorithm is proposed for sparse labeling of pixel and boundaries of
superpixels. Because over-segmentation errors are in general more tolerable and
easier to correct than the under-segmentation errors, our algorithm is designed
to prioritize minimization of false-merges over false-split mistakes. Our
experiments on both 2D and 3D data suggest that the proposed method yields
segmentation outputs that are more amenable to neural reconstruction than those
of existing methods.

本研究提出一种新型的 EM 分割分类器训练算法，旨在降低对标注和错误修正任务的手动劳动需求，实验表明该方法较现有方法更适于神经重建。

电子显微镜分割算法的理想属性

What Properties are Desirable from an Electron Microscopy Segmentation  Algorithm

Image segmentation refers to the process to divide an image into
nonoverlapping meaningful regions according to human perception, which has
become a classic topic since the early ages of computer vision. A lot of
research has been conducted and has resulted in many applications. However,
while many segmentation algorithms exist, yet there are only a few sparse and
outdated summarizations available, an overview of the recent achievements and
issues is lacking. We aim to provide a comprehensive review of the recent
progress in this field. Covering 180 publications, we give an overview of broad
areas of segmentation topics including not only the classic bottom-up
approaches, but also the recent development in superpixel, interactive methods,
object proposals, semantic image parsing and image cosegmentation. In addition,
we also review the existing influential datasets and evaluation metrics.
Finally, we suggest some design flavors and research directions for future
research in image segmentation.

本篇论文介绍了计算机视觉中图像分割的研究进展及方法，概述了底层的分割方法和最近发展的超像素、交互式方法、对象提案、语义图像解析和图像共分割，并对现有的数据集和评价指标进行了评估和总结，最后提出了面向未来研究的设计方向和研究方向。