Interphase Cell Cycle Staging using Deep Learning

Cell Cycle Stage Classification Using Phase Imaging with Computational Specificity

“…Compared to the overall accuracy of 0.91 from a method that used convolutional neural networks on fluorescence image pairs to classify single cells into “G1/S” or “G2”, our method achieved a comparable overall accuracy of 0.89 (Figure S4A). Compared to the F1-score of 0.94 and 0.88 for “G1” and “S/G2” respectively from a method that used convolution neural networks on fluorescence images, our method achieved a lower F-1 score for “G1” and a comparable F-1 score for “S/G2/M” (Figure.S4B). Compared to the method that classifies single-cell images from flow cytometry, our method achieved a lower F-1 score for “G1” and “G2/M” and a higher F-1 score for “S”.…”

“…Compared to many existing methods of cell cycle detection, − ,,,,, our method yielded comparable accuracy for at least one stage in the cell cycle interphase. The errors in our PICS inference can be corrected when the time-lapse progression and QPI measurements of cell morphology were taken into consideration.…”

“…A variety of signal processing techniques, including support vector machine (SVM), intensity histogram and intensity surface curvature, level-set segmentation, and k-nearest neighbor, have been applied to fluorescence intensity images to perform classification. In recent years, with the rapid development of parallel-computing capability and deep learning algorithms, convolutional neural networks have also been applied to fluorescence images of single cells for cell cycle tracking. , Since all these methods are based on fluorescence microscopy, they inevitably face the associated limitations, including photobleaching, chemical, and phototoxicity, weak fluorescent signals that require large exposures, as well as nonspecific binding. These constraints limit the applicability of fluorescence imaging to studying live cell cultures over large temporal scales …”