An Unsupervised Approach to Detect Microglia Tip in Volumetric Fluorescence Imaging Data
An Unsupervised Approach to Detect Microglia Tip in Volumetric Fluorescence Imaging Data
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DOI：
10.1109/isbi52829.2022.9761491
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发表时间：
2022-03
期刊：
影响因子：
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通讯作者：
Mengfan Wang;Kathleen Whiting;F. Lischka;Z. Galdzicki;Guoqiang Yu
中科院分区：
文献类型：
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作者：
Mengfan Wang;Kathleen Whiting;F. Lischka;Z. Galdzicki;Guoqiang Yu
小胶质细胞在维持大脑健康方面发挥着关键作用，小胶质细胞尖端的检测对于分析其运动性至关重要。然而，当前的尖端检测方法要么依赖于需要耗时注释的深度神经网络，要么是分析局部模式的无监督方法，其受到小胶质细胞形态变化的显着影响。在本文中，我们提出了一种无监督的多尺度小胶质细胞尖端检测方法。我们的方法测量候选尖端与相邻像素的凸包之间的距离，以消除形态变化的影响。我们展示了体积荧光成像数据的新方法，并与同行算法相比取得了卓越的性能。
Microglia play a key role in maintaining brain health, and the detection of microglia tips is essential for analyzing their motility. However, current tip detection methods either rely on deep neural networks, which require time-consuming annotations, or are unsupervised methods analyzing local patterns, which are significantly influenced by the microglia morphology change. In this paper, we propose an unsupervised, multi-scale microglia tip detection approach. Our approach measures the distance between the candidate tip and the convex hull of adjacent pixels to eliminate the influence of morphology variation. We demonstrated the new approach on volumetric fluorescence imaging data and achieved superior performance compared to peer algorithms.