Xie Yuanpu, Kong Xiangfei, Xing Fuyong, Liu Fujun, Su Hai, Yang Lin
J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, FL 32611, USA.
Department of Electrical and Computer Engineering, University of Florida, FL 32611, USA.
Med Image Comput Comput Assist Interv. 2015 Oct;9351:374-382. doi: 10.1007/978-3-319-24574-4_45. Epub 2015 Nov 18.
Robust and accurate nuclei localization in microscopy image can provide crucial clues for accurate computer-aid diagnosis. In this paper, we propose a convolutional neural network (CNN) based hough voting method to localize nucleus centroids with heavy cluttering and morphologic variations in microscopy images. Our method, which we name as deep voting, mainly consists of two steps. (1) Given an input image, our method assigns each local patch several pairs of voting vectors which indicate the positions it votes to, and the corresponding voting (used to weight each votes), our model can be viewed as an implicit hough-voting codebook. (2) We collect the weighted votes from all the testing patches and compute the final voting density map in a way similar to Parzen-window estimation. The final nucleus positions are identified by searching the local maxima of the density map. Our method only requires a few annotation efforts (just one click near the nucleus center). Experiment results on Neuroendocrine Tumor (NET) microscopy images proves the proposed method to be state-of-the-art.
在显微镜图像中进行稳健且准确的细胞核定位可为精确的计算机辅助诊断提供关键线索。在本文中，我们提出了一种基于卷积神经网络（CNN）的霍夫投票方法，用于在显微镜图像中定位存在严重 clutter 和形态变化的细胞核质心。我们的方法，我们称之为深度投票，主要由两个步骤组成。（1）给定输入图像，我们的方法为每个局部补丁分配几对投票向量，这些向量指示其投票的位置，以及相应的投票（用于加权每个投票），我们的模型可以被视为一个隐式霍夫投票码本。（2）我们从所有测试补丁中收集加权投票，并以类似于 Parzen 窗口估计的方式计算最终的投票密度图。通过搜索密度图的局部最大值来识别最终的细胞核位置。我们的方法只需要很少的标注工作（只需在细胞核中心附近点击一下）。在神经内分泌肿瘤（NET）显微镜图像上的实验结果证明了所提出的方法是最先进的。
