Department of Computer Science, Old Dominion University, Norfolk, VA 23529, USA.
Allen Institute for Brain Science, Seattle, WA 98103, USA.
Bioinformatics. 2016 Aug 1;32(15):2352-8. doi: 10.1093/bioinformatics/btw165. Epub 2016 Mar 25.
Accurate segmentation of brain electron microscopy (EM) images is a critical step in dense circuit reconstruction. Although deep neural networks (DNNs) have been widely used in a number of applications in computer vision, most of these models that proved to be effective on image classification tasks cannot be applied directly to EM image segmentation, due to the different objectives of these tasks. As a result, it is desirable to develop an optimized architecture that uses the full power of DNNs and tailored specifically for EM image segmentation.
In this work, we proposed a novel design of DNNs for this task. We trained a pixel classifier that operates on raw pixel intensities with no preprocessing to generate probability values for each pixel being a membrane or not. Although the use of neural networks in image segmentation is not completely new, we developed novel insights and model architectures that allow us to achieve superior performance on EM image segmentation tasks. Our submission based on these insights to the 2D EM Image Segmentation Challenge achieved the best performance consistently across all the three evaluation metrics. This challenge is still ongoing and the results in this paper are as of June 5, 2015.
https://github.com/ahmed-fakhry/dive
准确分割脑电子显微镜 (EM) 图像是密集电路重建的关键步骤。尽管深度神经网络 (DNN) 在计算机视觉的许多应用中得到了广泛的应用，但由于这些任务的目标不同，大多数在图像分类任务中被证明有效的模型不能直接应用于 EM 图像分割。因此，开发一种优化的架构，利用 DNN 的全部功能，并专门针对 EM 图像分割进行定制，是很有必要的。
在这项工作中，我们为这个任务提出了一种新的 DNN 设计。我们训练了一个像素分类器，它对原始像素强度进行操作，无需预处理即可生成每个像素是否为膜的概率值。虽然神经网络在图像分割中的应用并不是全新的，但我们开发了新的见解和模型架构，使我们能够在 EM 图像分割任务中实现卓越的性能。我们基于这些见解提交的 2D EM 图像分割挑战赛在所有三个评估指标上始终表现出最佳性能。这个挑战仍在进行中，本文中的结果截止到 2015 年 6 月 5 日。
https://github.com/ahmed-fakhry/dive
