Electron microscopic connectomics is an ambitious research direction with the
goal of studying comprehensive brain connectivity maps by using
high-throughput, nano-scale microscopy. One of the main challenges in
connectomics research is developing scalable image analysis algorithms that
require minimal user intervention. Recently, deep learning has drawn much
attention in computer vision because of its exceptional performance in image
classification tasks. For this reason, its application to connectomic analyses
holds great promise, as well. In this paper, we introduce a novel deep neural
network architecture, FusionNet, for the automatic segmentation of neuronal
structures in connectomics data. FusionNet leverages the latest advances in
machine learning, such as semantic segmentation and residual neural networks,
with the novel introduction of summation-based skip connections to allow a much
deeper network architecture for a more accurate segmentation. We demonstrate
the performance of the proposed method by comparing it with state-of-the-art
electron microscopy (EM) segmentation methods from the ISBI EM segmentation
challenge. We also show the segmentation results on two different tasks
including cell membrane and cell body segmentation and a statistical analysis
of cell morphology.

本研究介绍了一种基于深度学习的新型神经网络结构 FusionNet，用于自动分割连接组学数据中的神经元结构，并表明其在电子显微镜 (EM) 图像分割方面具有优异的性能。

FusionNet: 一种用于连接组学中图像分割的深度完全残差卷积神经网络

FusionNet: A deep fully residual convolutional neural network for image  segmentation in connectomics

There is large consent that successful training of deep networks requires
many thousand annotated training samples. In this paper, we present a network
and training strategy that relies on the strong use of data augmentation to use
the available annotated samples more efficiently. The architecture consists of
a contracting path to capture context and a symmetric expanding path that
enables precise localization. We show that such a network can be trained
end-to-end from very few images and outperforms the prior best method (a
sliding-window convolutional network) on the ISBI challenge for segmentation of
neuronal structures in electron microscopic stacks. Using the same network
trained on transmitted light microscopy images (phase contrast and DIC) we won
the ISBI cell tracking challenge 2015 in these categories by a large margin.
Moreover, the network is fast. Segmentation of a 512x512 image takes less than
a second on a recent GPU. The full implementation (based on Caffe) and the
trained networks are available at
this http URL .

该研究提出了一种基于数据增强的网络和训练策略，用于更有效地利用可用的标注样本，以实现对神经结构的分割和细胞跟踪。研究表明，这种网络可以从极少量的图像中进行端对端的训练，并在电子显微图像和透射光显微图像上均取得了优异的分割和跟踪效果，且速度快。