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首页> 外文期刊>Micron: The international research and review journal for microscopy >Automated stitching of microscope images of fluorescence in cells with minimal overlap
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Automated stitching of microscope images of fluorescence in cells with minimal overlap

机译：微量重叠的细胞中荧光显微镜图像的自动缝合

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The morphology of tumor cells is highly related to their phenotype and activity. To verify the drug response of a brain tumor patient, fluorescence microscope images of drug-treated patient-derived cells in each well are analyzed. Due to the limitation of the field of view (FOV), a large number of small FOVs are acquired to compose one complete microscope well. Here, we propose an automated method for accurately stitching tile-scanned fluorescence microscope images, even with noise and a narrow overlapping region between adjacent fields. The proposed method is based on intensity-based normalized cross-correlation (NCC) and a triangular method-based threshold. The proposed method's quantitative accuracy and the sensitivity of the input was compared to other existing stitching tools, MIST and FijiIS, setting manually stitched images as the ground truth. The test images were 20 samples of 3 x 3 grid images in three versions of the fluorescence channel. The distance between the location of each field and number of cells was determined for different input field overlap ranges (1%, 3%, 5%, and 10%), while the actual value was about 1.15%. The proposed method had a distance error of 1.5 pixels at an input overlap of 1%, showing the lowest minimum error at all channels. Regarding the difference in cell numbers, although the number of overlapping cells was always small because of the narrow overlapping range, the proposed method was able to generate the resultant image with the smallest difference. In addition, to confirm the size limitation of the proposed algorithm, the accuracy of stitching images of grid structures 3 x 3, 5 x 5, 10 x 10-20 x 20 was tested, showing consistent results: In conclusion, quantitative evaluation of the performance of the method proved its improved accuracy compared to other current state-of-art techniques, and it showed robust performance even with noise and a narrow overlapping region between adjacent fields.
机译：肿瘤细胞的形态与其表型和活性有高度相关。为了验证脑肿瘤患者的药物响应，分析了每个孔中药物治疗的患者衍生细胞的荧光显微镜图像。由于视场（FOV）的限制，获取大量小FOV来组成一个完整的显微镜。这里，我们提出了一种用于精确地缝合瓷砖扫描的荧光显微镜图像的自动化方法，即使在相邻场之间的噪声和窄的重叠区域也是如此。该方法基于基于强度的归一化互相关（NCC）和基于三角形方法的阈值。该方法的定量准确度和输入的敏感性与其他现有的缝合工具，雾和斐热镜相比，将手动缝合图像设置为地面真理。在荧光通道的三个版本中，测试图像为3×3网格图像的20个样本。针对不同的输入场重叠范围（1％，3％，5％和10％）确定每个场的位置与细胞数之间的距离，而实际值为约1.15％。该方法的距离误差为1.5像素，输入重叠为1％，显示所有通道处的最低最小误差。关于细胞数的差异，尽管由于重叠范围窄，重叠单元的数量总是小，但是所提出的方法能够以最小的差异产生所得图像。另外，为了确认所提出的算法的尺寸限制，测试了网格结构3×3,5×5,10×10×10×10×10×10×10×10×10×10×10×10×10×10×10×10×10×10×10×10×10的精度，结果是：总之，定量评估与其他当前最先进的技术相比，该方法的性能证明了其提高的准确性，并且即使在相邻场之间的噪声和窄重叠区域也表现出稳健的性能。

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