Deep models for brain EM image segmentation: novel insights and improved performance
Deep models for brain EM image segmentation: novel insights and improved performance
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DOI：
10.1093/bioinformatics/btw165
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发表时间：
2016-08-01
期刊：
影响因子：
5.8
通讯作者：
Ji, Shuiwang
中科院分区：
文献类型：
--
作者：
Fakhry, Ahmed;Peng, Hanchuan;Ji, Shuiwang
动机：脑电子显微镜（EM）图像的准确分割是密集电路重建的关键步骤。虽然深度神经网络（DNN）已广泛应用于计算机视觉的许多应用中，但由于这些任务的目标不同，大多数被证明对图像分类任务有效的模型不能直接应用于EM图像分割。因此，我们希望开发一种优化的架构，使用DNN的全部功能，并专门为EM图像segmentation.Results量身定制：在这项工作中，我们提出了一种新的DNN设计这项任务。我们训练了一个像素分类器，该分类器在没有预处理的情况下对原始像素强度进行操作，以生成每个像素是否为膜的概率值。虽然在图像分割中使用神经网络并不完全是新的，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上级性能。基于这些见解，我们提交的2D EM图像分割挑战赛在所有三个评估指标上都取得了最佳表现。这一挑战仍在继续，本文中的结果截至2015年6月5日。可用性和实施：https://github.com/ahmed-fakhry/dive
Motivation: 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.Results: 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.Availability and Implementation: https://github.com/ahmed-fakhry/dive