Background intensity correction for terabyte-sized time-lapse images.

Summary Several computational challenges associated with large-scale background image correction of terabyte-sized fluorescent images are discussed and analysed in this paper. Dark current, flat-field and background correction models are applied over a mosaic of hundreds of spatially overlapping fields of view (FOVs) taken over the course of several days, during which the background diminishes as cell colonies grow. The motivation of our work comes from the need to quantify the dynamics of OCT-4 gene expression via a fluorescent reporter in human stem cell colonies. Our approach to background correction is formulated as an optimization problem over two image partitioning schemes and four analytical correction models. The optimization objective function is evaluated in terms of (1) the minimum root mean square (RMS) error remaining after image correction, (2) the maximum signal-to-noise ratio (SNR) reached after downsampling and (3) the minimum execution time. Based on the analyses with measured dark current noise and flat-field images, the most optimal GFP background correction is obtained by using a data partition based on forming a set of submosaic images with a polynomial surface background model. The resulting image after correction is characterized by an RMS of about 8, and an SNR value of a 4 × 4 downsampling above 5 by Rose criterion. The new technique generates an image with half RMS value and double SNR value when compared to an approach that assumes constant background throughout the mosaic. We show that the background noise in terabyte-sized fluorescent image mosaics can be corrected computationally with the optimized triplet (data partition, model, SNR driven downsampling) such that the total RMS value from background noise does not exceed the magnitude of the measured dark current noise. In this case, the dark current noise serves as a benchmark for the lowest noise level that an imaging system can achieve. In comparison to previous work, the past fluorescent image background correction methods have been designed for single FOV and have not been applied to terabyte-sized images with large mosaic FOVs, low SNR and diminishing access to background information over time as cell colonies span entirely multiple FOVs. The code is available as open-source from the following link https://isg.nist.gov/. Lay Description In this paper we present background intensity correction for terabyte-sized time-lapse fluorescent images. The motivation of our work comes from the need to quantify the dynamics of OCT-4 gene expression via a fluorescent reporter in human stem cell colonies. The challenges lie in correcting time-lapse fluorescent images of individual size about 462 Megapixels that have been assembled from hundreds of spatially overlapping smaller fields of view (FOVs) taken over the course of several days. Furthermore, the background diminishes as cell colonies grow over time as observed during the acquisition of three time-lapse replicates equal about 2.6 terabytes. Our approach to background correction is formulated as an optimization problem where the objective function is evaluated in terms of (1) the remaining error after image correction, (2) the maximum Signal-to-Noise Ratio (SNR) reached after binning the image, and (3) the minimum execution time. The new technique generates an image with half Root-Mean-Square (RMS) value and double SNR value when compared to a typical approach that assumes constant background throughout the mosaic. We show that the background noise after correction does not exceed the magnitude of the measured dark current noise. In this case, the dark current noise serves as a benchmark for the lowest noise level that an imaging system can achieve.