Spherical-Patches Extraction for Deep-Learning-Based Critical Points Detection in 3D Neuron Microscopy Images

作者：Weixun Chen, Min Liu, Qi Zhan, Yinghui Tan, Erik Meijering, Miroslav Radojević, Yaonan Wang · 发表于：IEEE Transactions on Medical Imaging · 年份：2020 · DOI：10.1109/tmi.2020.3031289 · 被引用次数：29 · 研究领域：Cell Image Analysis Techniques、Image Processing Techniques and Applications、Advanced Fluorescence Microscopy Techniques

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 ...