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首页> 外文期刊>IEEE Transactions on Medical Imaging >Spherical-Patches Extraction for Deep-Learning-Based Critical Points Detection in 3D Neuron Microscopy Images
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Spherical-Patches Extraction for Deep-Learning-Based Critical Points Detection in 3D Neuron Microscopy Images

机译：三维神经元显微镜图像中基于深度学习的关键点检测的球形斑块

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摘要

Digital reconstruction of neuronal structures is very important to neuroscience research. Many existing reconstruction algorithms require a set of good seed points. 3D neuron critical points, including terminations, branch points and cross-over points, are good candidates for such seed points. However, a method that can simultaneously detect all types of critical points has barely been explored. In this work, we present a method to simultaneously detect all 3 types of 3D critical points in neuron microscopy images, based on a spherical-patches extraction (SPE) method and a 2D multi-stream convolutional neural network (CNN). SPE uses a set of concentric spherical surfaces centered at a given critical point candidate to extract intensity distribution features around the point. Then, a group of 2D spherical patches is generated by projecting the surfaces into 2D rectangular image patches according to the orders of the azimuth and the polar angles. Finally, a 2D multi-stream CNN, in which each stream receives one spherical patch as input, is designed to learn the intensity distribution features from those spherical patches and classify the given critical point candidate into one of four classes: termination, branch point, cross-over point or non-critical point. Experimental results confirm that the proposed method outperforms other state-of-the-art critical points detection methods. The critical points based neuron reconstruction results demonstrate the potential of the detected neuron critical points to be good seed points for neuron reconstruction. Additionally, we have established a public dataset dedicated for neuron critical points detection, which has been released along with this article.
机译：数字重建神经元结构对神经科学研究非常重要。许多现有的重建算法需要一组好的种子点。 3D神经元关键点，包括终端，分支点和交叉点，是这种种子点的良好候选者。然而，可以同时检测所有类型关键点的方法几乎没有探索。在这项工作中，我们提出了一种方法来同时检测神经元显微镜图像中所有3种类型的3D关键点，基于球形贴片提取（SPE）方法和2D多流卷积神经网络（CNN）。 SPE使用一组以给定的关键点候选的同心球形表面，以提取各个点的强度分布特征。然后，通过根据方位角和极性角度突出到2D矩形图像贴片来产生一组2D球形贴片。最后，旨在将每个流接收一个球形贴片作为输入的2D多流CNN，旨在学习来自那些球形贴片的强度分布特征，并将给定的关键点候选分类为四个类：终止，分支点之一，交叉点或非关键点。实验结果证实，所提出的方法优于其他最先进的关键点检测方法。基于关键点的神经元重建结果证明了检测到的神经元关键点的潜力是神经元重建的好的种子点。此外，我们已经建立了专门用于神经元关键点检测的公共数据集，这与本文一起发布。

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