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

Electron Microscopy (EM) image (or volume) segmentation has become
significantly important in recent years as an instrument for connectomics. This
paper proposes a novel agglomerative framework for EM segmentation. In
particular, given an over-segmented image or volume, we propose a novel
framework for accurately clustering regions of the same neuron. Unlike existing
agglomerative methods, the proposed context-aware algorithm divides superpixels
(over-segmented regions) of different biological entities into different
subsets and agglomerates them separately. In addition, this paper describes a
"delayed" scheme for agglomerative clustering that postpones some of the merge
decisions, pertaining to newly formed bodies, in order to generate a more
confident boundary prediction. We report significant improvements attained by
the proposed approach in segmentation accuracy over existing standard methods
on 2D and 3D datasets.

该论文提出了一种新颖的聚合框架用于电子显微镜图像（或体积）分割，即给定一个过分割的图像或体积，我们提出了一个新的框架来准确地聚类相同神经元的区域，并且在 2D，3D 数据集上，与现有标准方法相比，该方法在分割准确度上取得了显着的改进。

一种上下文感知的电子显微镜分割延迟凝聚框架

A Context-aware Delayed Agglomeration Framework for Electron Microscopy  Segmentation

Automated sample preparation and electron microscopy enables acquisition of
very large image data sets. These technical advances are of special importance
to the field of neuroanatomy, as 3D reconstructions of neuronal processes at
the nm scale can provide new insight into the fine grained structure of the
brain. Segmentation of large-scale electron microscopy data is the main
bottleneck in the analysis of these data sets. In this paper we present a
pipeline that provides state-of-the art reconstruction performance while
scaling to data sets in the GB-TB range. First, we train a random forest
classifier on interactive sparse user annotations. The classifier output is
combined with an anisotropic smoothing prior in a Conditional Random Field
framework to generate multiple segmentation hypotheses per image. These
segmentations are then combined into geometrically consistent 3D objects by
segmentation fusion. We provide qualitative and quantitative evaluation of the
automatic segmentation and demonstrate large-scale 3D reconstructions of
neuronal processes from a $\mathbf{27,000}$ $\mathbf{\mu m^3}$ volume of brain
tissue over a cube of $\mathbf{30 \; \mu m}$ in each dimension corresponding to
1000 consecutive image sections. We also introduce Mojo, a proofreading tool
including semi-automated correction of merge errors based on sparse user
scribbles.

自动样品制备和电子显微镜使得大规模图像数据采集成为可能，基于此，本文提出了一种 pipeline 系统，通过结合随机森林分类和条件随机场框架，能够完成大规模电子显微镜数据的分割和自动化标注，从而实现了大规模 3D 神经元的重建和可视化。