William A. Brookshire Department of Chemical and Biomolecular Engineering, University of Houston, Houston, TX 77204, United States.
Department of Electrical and Computer Engineering, University of Houston, Houston, TX 77204, United States.
Bioinformatics. 2024 Aug 2;40(8). doi: 10.1093/bioinformatics/btae467.
High-throughput time-lapse imaging is a fundamental tool for efficient living cell profiling at single-cell resolution. Label-free phase-contrast video microscopy enables noninvasive, nontoxic, and long-term imaging. The tradeoff between speed and throughput, however, implies that despite the state-of-the-art autofocusing algorithms, out-of-focus cells are unavoidable due to the migratory nature of immune cells (velocities >10 μm/min). Here, we propose PostFocus to (i) identify out-of-focus images within time-lapse sequences with a classifier, and (ii) deploy a de-noising diffusion probabilistic model to yield reliable in-focus images.
De-noising diffusion probabilistic model outperformed deep discriminative models with a superior performance on the whole image and around cell boundaries. In addition, PostFocus improves the accuracy of image analysis (cell and contact detection) and the yield of usable videos.
Open-source code and sample data are available at: https://github.com/kwu14victor/PostFocus.
高通量延时成像技术是在单细胞分辨率下进行高效活细胞分析的基本工具。无标记相差视频显微镜能够进行非侵入性、无毒且长期的成像。然而，速度和通量之间的权衡意味着，尽管采用了最先进的自动聚焦算法，但由于免疫细胞的迁移特性（速度>10μm/min），不可避免地会出现离焦细胞。在这里，我们提出 PostFocus 来：(i)使用分类器在延时序列中识别离焦图像，和 (ii) 部署去噪扩散概率模型以产生可靠的聚焦图像。
去噪扩散概率模型的表现优于深度判别模型，在整个图像和细胞边界周围都具有更好的性能。此外，PostFocus 提高了图像分析（细胞和接触检测）的准确性和可用视频的产量。
可在以下网址获得开源代码和示例数据：https://github.com/kwu14victor/PostFocus。
