IEEE Trans Med Imaging. 2017 Feb;36(2):447-456. doi: 10.1109/TMI.2016.2613019. Epub 2016 Sep 23.
Accurate reconstruction of anatomical connections between neurons in the brain using electron microscopy (EM) images is considered to be the gold standard for circuit mapping. A key step in obtaining the reconstruction is the ability to automatically segment neurons with a precision close to human-level performance. Despite the recent technical advances in EM image segmentation, most of them rely on hand-crafted features to some extent that are specific to the data, limiting their ability to generalize. Here, we propose a simple yet powerful technique for EM image segmentation that is trained end-to-end and does not rely on prior knowledge of the data. Our proposed residual deconvolutional network consists of two information pathways that capture full-resolution features and contextual information, respectively. We showed that the proposed model is very effective in achieving the conflicting goals in dense output prediction; namely preserving full-resolution predictions and including sufficient contextual information. We applied our method to the ongoing open challenge of 3D neurite segmentation in EM images. Our method achieved one of the top results on this open challenge. We demonstrated the generality of our technique by evaluating it on the 2D neurite segmentation challenge dataset where consistently high performance was obtained. We thus expect our method to generalize well to other dense output prediction problems.
使用电子显微镜 (EM) 图像准确重建大脑神经元之间的解剖连接被认为是电路映射的黄金标准。获得重建的关键步骤是能够自动分割神经元，其精度接近人类水平。尽管最近在 EM 图像分割方面取得了技术进展，但大多数技术在某种程度上都依赖于特定于数据的手工制作特征，限制了它们的泛化能力。在这里，我们提出了一种简单而强大的 EM 图像分割技术，该技术是端到端训练的，不依赖于数据的先验知识。我们提出的残差去卷积网络由两个信息路径组成，分别捕获全分辨率特征和上下文信息。我们表明，所提出的模型在实现密集输出预测中的相互冲突的目标方面非常有效；即保留全分辨率预测并包含足够的上下文信息。我们将我们的方法应用于正在进行的 EM 图像中 3D 神经突分割的公开挑战。我们的方法在该公开挑战中取得了其中一项最佳成绩。我们通过在 2D 神经突分割挑战数据集上评估该技术来证明其通用性，在该数据集上始终获得了很高的性能。因此，我们期望我们的方法能够很好地推广到其他密集输出预测问题。
