基于补丁的荧光图像小动脉和小静脉语义分割。

Yasmin M Kassim, Olga V Glinskii, Vladislav V Glinsky, Virginia H Huxley, Kannappan Palaniappan
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引用次数: 1

摘要

微血管结构的分割和量化是研究微血管重构的主要步骤。提出的基于补丁的语义架构能够对具有挑战性的荧光显微镜图像进行准确的分割。我们基于像素的快速语义网络对来自不同荧光图像的随机斑块进行训练,学习如何区分血管和非血管像素。本文提出的语义血管网络(SVNet)依赖于理解斑块中细血管的形态结构,而不是将整个图像作为输入,以加快训练过程并保持薄结构的清晰度。对去卵巢小鼠硬脑膜荧光显微成像的实验结果显示,小动脉和小静脉部分均有良好的结果。我们将结果与不同的分割方法进行了比较,如局部分割、全局阈值分割、基于匹配的过滤方法和相关的最先进的深度学习网络。我们的总体准确率(> 98%)优于所有方法,包括我们之前的工作(VNet)。[1]。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Patch-Based Semantic Segmentation for Detecting Arterioles and Venules in Epifluorescence Imagery.

Patch-Based Semantic Segmentation for Detecting Arterioles and Venules in Epifluorescence Imagery.

Patch-Based Semantic Segmentation for Detecting Arterioles and Venules in Epifluorescence Imagery.

Patch-Based Semantic Segmentation for Detecting Arterioles and Venules in Epifluorescence Imagery.

Segmentation and quantification of microvasculature structures are the main steps toward studying microvasculature remodeling. The proposed patch based semantic architecture enables accurate segmentation for the challenging epifluorescence microscopy images. Our pixel-based fast semantic network trained on random patches from different epifluorescence images to learn how to discriminate between vessels versus nonvessels pixels. The proposed semantic vessel network (SVNet) relies on understanding the morphological structure of the thin vessels in the patches rather than considering the whole image as input to speed up the training process and to maintain the clarity of thin structures. Experimental results on our ovariectomized - ovary removed (OVX) - mice dura mater epifluorescence microscopy images shows promising results in both arteriole and venule part. We compared our results with different segmentation methods such as local, global thresholding, matched based filter approaches and related state of the art deep learning networks. Our overall accuracy (> 98%) outperforms all the methods including our previous work (VNet). [1].

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