摘要翻译：
少突胶质细胞祖细胞（OPCs）的可靠计数和分割是关键的图像分析步骤，有可能解开OPC在病理过程中的功能之谜。提出了一种基于显著性的OPCs检测方法，并采用标记控制的分水岭算法对OPCs进行分割。该方法首先在单独的信道上实现频率调谐显著性检测，以获得候选小区的区域。最终的检测结果和内部标记可以通过组合来自不同显著图的信息来计算。本文提出了OPCs的最佳显着性水平(OSLO)。这里，分水岭分割是用有效的内部标记高效地执行的。实验表明，我们的方法在精度方面优于现有的方法。
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英文标题：
《OSLO: Automatic Cell Counting and Segmentation for Oligodendrocyte
  Progenitor Cells》
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作者：
Haoyi Ma, Rebecca Beiter, Alban Gaultier, Scott T. Acton and Zongli
  Lin
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最新提交年份：
2018
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分类信息：

一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Image and Video Processing        图像和视频处理
分类描述：Theory, algorithms, and architectures for the formation, capture, processing, communication, analysis, and display of images, video, and multidimensional signals in a wide variety of applications. Topics of interest include: mathematical, statistical, and perceptual image and video modeling and representation; linear and nonlinear filtering, de-blurring, enhancement, restoration, and reconstruction from degraded, low-resolution or tomographic data; lossless and lossy compression and coding; segmentation, alignment, and recognition; image rendering, visualization, and printing; computational imaging, including ultrasound, tomographic and magnetic resonance imaging; and image and video analysis, synthesis, storage, search and retrieval.
用于图像、视频和多维信号的形成、捕获、处理、通信、分析和显示的理论、算法和体系结构。感兴趣的主题包括：数学，统计，和感知图像和视频建模和表示；线性和非线性滤波、去模糊、增强、恢复和重建退化、低分辨率或层析数据；无损和有损压缩编码；分割、对齐和识别；图像渲染、可视化和打印；计算成像，包括超声、断层和磁共振成像；以及图像和视频的分析、合成、存储、搜索和检索。
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英文摘要：
  Reliable cell counting and segmentation of oligodendrocyte progenitor cells (OPCs) are critical image analysis steps that could potentially unlock mysteries regarding OPC function during pathology. We propose a saliency-based method to detect OPCs and use a marker-controlled watershed algorithm to segment the OPCs. This method first implements frequency-tuned saliency detection on separate channels to obtain regions of cell candidates. Final detection results and internal markers can be computed by combining information from separate saliency maps. An optimal saliency level for OPCs (OSLO) is highlighted in this work. Here, watershed segmentation is performed efficiently with effective internal markers. Experiments show that our method outperforms existing methods in terms of accuracy.
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PDF链接：
https://arxiv.org/pdf/1802.05321