Information Technology Laboratory, National Institute of Standards and Technology, 100 Bureau Dr, Gaithersburg, 20878, MD, USA.
Physical Measurement Laboratory, National Institute of Standards and Technology, 100 Bureau Drive, Gaithersburg, 20899, MD, USA.
Sci Rep. 2017 Jul 10;7(1):4988. doi: 10.1038/s41598-017-04567-y.
Automated microscopy can image specimens larger than the microscope's field of view (FOV) by stitching overlapping image tiles. It also enables time-lapse studies of entire cell cultures in multiple imaging modalities. We created MIST (Microscopy Image Stitching Tool) for rapid and accurate stitching of large 2D time-lapse mosaics. MIST estimates the mechanical stage model parameters (actuator backlash, and stage repeatability 'r') from computed pairwise translations and then minimizes stitching errors by optimizing the translations within a (4r) square area. MIST has a performance-oriented implementation utilizing multicore hybrid CPU/GPU computing resources, which can process terabytes of time-lapse multi-channel mosaics 15 to 100 times faster than existing tools. We created 15 reference datasets to quantify MIST's stitching accuracy. The datasets consist of three preparations of stem cell colonies seeded at low density and imaged with varying overlap (10 to 50%). The location and size of 1150 colonies are measured to quantify stitching accuracy. MIST generated stitched images with an average centroid distance error that is less than 2% of a FOV. The sources of these errors include mechanical uncertainties, specimen photobleaching, segmentation, and stitching inaccuracies. MIST produced higher stitching accuracy than three open-source tools. MIST is available in ImageJ at isg.nist.gov.
自动化显微镜可以通过拼接重叠的图像块来对超出显微镜视场 (FOV) 的标本进行成像。它还可以在多种成像模式下对整个细胞培养物进行延时研究。我们创建了 MIST（显微镜图像拼接工具），用于快速准确地拼接大型二维延时镶嵌图。MIST 根据计算出的两两平移来估计机械台模型参数（执行器间隙和台重复性'r'），然后通过在（4r）正方形区域内优化平移来最小化拼接误差。MIST 具有面向性能的实现，利用多核混合 CPU/GPU 计算资源，可以比现有工具快 15 到 100 倍地处理数 TB 的延时多通道镶嵌图。我们创建了 15 个参考数据集来量化 MIST 的拼接准确性。这些数据集由三个以低密度接种的干细胞集落准备组成，并以不同的重叠（10%至 50%）进行成像。测量 1150 个菌落的位置和大小以量化拼接准确性。MIST 生成的拼接图像的平均质心距离误差小于 FOV 的 2%。这些误差的来源包括机械不确定性、标本光漂白、分割和拼接不准确。MIST 的拼接准确性高于三个开源工具。MIST 可在 ImageJ 中从 isg.nist.gov 获取。
