Deep Learning Segmentation of Optical Microscopy Images Improves 3-D Neuron Reconstruction
Deep Learning Segmentation of Optical Microscopy Images Improves 3-D Neuron Reconstruction
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DOI：
10.1109/tmi.2017.2679713
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发表时间：
2017-07-01
影响因子：
10.6
通讯作者：
Ji, Shuiwang
中科院分区：
文献类型：
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作者：
Li, Rongjian;Zeng, Tao;Ji, Shuiwang
根据显微图像对 3D 神经元结构进行数字重建或追踪，是逆向工程大脑布线和解剖学的关键一步。尽管之前进行了多次尝试，但这项任务仍然非常具有挑战性，特别是当图像被噪声污染或神经突图案片段不连续时。解决此类问题的一种方法是在应用追踪或重建技术之前使用图像分割方法来识别神经元体素的位置。该预处理步骤有望消除数据中的噪声，从而改善重建结果。在本文中，我们建议使用 3D 卷积神经网络 (CNN) 来分割神经元显微镜图像。具体来说，我们设计了一种新颖的 CNN 架构，它将体积图像作为输入，将其体素分割图作为输出。开发的架构使我们能够以端到端的方式使用大型显微镜图像进行训练和预测。我们评估了我们的模型在来自不同生物体的各种具有挑战性的 3D 显微镜图像上的性能。结果表明，当与不同的重建算法结合时，所提出的方法显着提高了跟踪性能。
Digital reconstruction, or tracing, of 3-D neuron structure from microscopy images is a critical step toward reversing engineering the wiring and anatomy of a brain. Despite a number of prior attempts, this task remains very challenging, especially when images are contaminated by noises or have discontinued segments of neurite patterns. An approach for addressing such problems is to identify the locations of neuronal voxels using image segmentation methods, prior to applying tracing or reconstruction techniques. This preprocessing step is expected to remove noises in the data, thereby leading to improved reconstruction results. In this paper, we proposed to use 3-D convolutional neural networks (CNNs) for segmenting the neuronal microscopy images. Specifically, we designed a novel CNN architecture, that takes volumetric images as the inputs and their voxel-wise segmentation maps as the outputs. The developed architecture allows us to train and predict using large microscopy images in an end-to-end manner. We evaluated the performance of our model on a variety of challenging 3-D microscopy images from different organisms. Results showed that the proposed methods improved the tracing performance significantly when combined with different reconstruction algorithms.