摘要
背景:由于弥散的沉积边界和成像变化,阿尔茨海默病特征性的淀粉样蛋白-β病理图像难以一致且准确地分割。 方法:我们评估了ImageSURF的性能,ImageSURF是我们的开源ImageJ插件,它考虑了一系列图像衍生物来训练图像分类器。我们将ImageSURF与标准图像阈值进行比较,以评估其在淀粉样蛋白病理学的荧光图像上的再现性,准确性和普遍性。 结果:ImageSURF对淀粉样蛋白-β图像的分割明显更加忠实,并且具有明显更高的普遍性,优于阈值优化。 结论:除了在捕获病理图像的人体评估方面的卓越性能之外,ImageSURF还能够以一致且无偏见的方式分割任何大小的图像集,而无需额外的盲法,并且可以回顾性地应用于现有图像。培训过程产生一个分类器文件,可以作为补充数据共享,允许完全开放的方法和数据,并实现不同研究之间更直接的比较。
关键词: 显微镜,定量,图像分割,阈值处理,机器学习,阿尔茨海默病。
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