Data Set for 'Self-Supervised Machine Learning for Live Cell Imagery Segmentation'

These images are for testing with the self-supervised machine learning demo code posted to GitHub.  The title and abstract for this work are as follows:

Self-Supervised Machine Learning for Live Cell Imagery Segmentation

Machine learning algorithms hold the promise of greatly improving live cell image analysis by way of (1) analyzing far more imagery than can be achieved by more traditional manual approaches and (2) by eliminating the subjective nature of researchers and diagnosticians selecting the cells or cell features to be included in the analyzed data set. Currently, however, even the most sophisticated algorithms require time-consuming user supervision at the front end, such that the subjectivity problem is not removed but rather incorporated into the algorithm’s initial training steps and then repeatedly applied to the imagery. To address this roadblock, we have developed a self-supervised machine learning (SSL) algorithm that recursively trains itself directly from the live cell imagery data, thus providing objective segmentation and quantification without the need for user configuration. The approach incorporates an optical flow algorithm component to self-label cell and background pixels for training, followed by the extraction of additional feature vectors for the automated generation of a cell/background classification model. Because it is self-trained, the software has no user-adjustable parameters and does not require curated training imagery.  The algorithm was applied to automatically segment cells from their background for a variety of cell types and five commonly used imaging modalities - fluorescence, phase contrast, differential interference contrast (DIC), transmitted light and interference reflection microscopy (IRM).  The approach is broadly applicable in that it enables completely automated cell segmentation for long-term live cell phenotyping applications, regardless of the input imagery’s optical modality, magnification or cell type – an important step towards accessible and configuration-free cell image analysis tools.