arXiv 1802.053212018-02eess.IV

OSLO：少突胶质祖细胞的自动细胞计数和分割

OSLO: Automatic Cell Counting and Segmentation for Oligodendrocyte Progenitor Cells

Haoyi Ma, Rebecca Beiter, Alban Gaultier, Scott T. Acton, Zongli Lin

备注：: v1: Submitted to ICIP 2018; v2: Accepted to be published in 2018 IEEE International Conference on Image Processing, Oct 7-10, 2018, Athens, Greece. IEEE Copyright notice added. Minor changes for camera-ready version

摘要：

可靠的细胞计数和少突胶质祖细胞（OPC）的分割是关键的图像分析步骤，可能会解开病理过程中OPC功能的奥秘。我们提出了一种基于显著性的方法来检测OPC和使用标记控制的分水岭算法分割OPC。该方法首先在不同的通道上实现频率调谐的显着性检测，以获得候选细胞的区域。最终的检测结果和内部标记可以通过组合来自单独的显著性图的信息来计算。在这项工作中，突出的最佳显着水平的OPC（OSLO）。在这里，分水岭分割有效地执行有效的内部标记。实验表明，我们的方法优于现有的方法的准确性。

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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