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

<p><strong>Self-supervised machine learning code and data for segmenting live cell imagery (Matlab)</strong></p> <p><em>Running the Code</em></p> <p>SSL_Demo_2.m : main program for self-supervised machine learning segmentation</p> <p>SSL_Declumping_2.m : main program for declumping application (applied to output of SSL_Demo_2.m)</p> <p>This Matlab code is designed to be used with time-resolved live cell microscopy images (tiffs) for the automated segmentation of cells from background.</p> <p>It is recommended you first run this code with its accompanying demo data (included in this package), keeping the current directory structure.</p> <p>Simply open SSL_Demo_2.m or SSL_Declumping_2.m in Matlab and hit Run.</p> <p><em>Code Methodology</em></p> <p>The principle of self-supervised machine learning is that you simply load your images and Run - no parameter tuning needed, no training imagery required.</p> <p>Run from start to finish, the SSL_Demo_2.m code uses consecutive pairs of images to generate training data of &#39;cells&#39; and &#39;background&#39; via dynamic feature vectors based on optical flow (unsupervised). These self-labeled pixels are then used to generate static feature vectors (entropy, gradient), which in turn are used to train a classifier model. The training data is updated every image in order to automatically adapt to temporal changes in cell morphologies or background illumination.</p> <p>The code was tested for high fidelity segmentation using five different modes of light microscopy: transmitted light, DIC, phase contrast, fluorescence and interference reflection microscopy.</p> <p>Six different cell lines were imaged to cover a range of morphologies and phenotypic dynamics using three cameras of differing resolutions.</p> <p>The associated manuscript for this work can be found here (although the latest version is under peer review as of this writing):&nbsp;</p> <p><a href="https://www.biorxiv.org/content/10.1101/2021.01.07.425773v1">https://www.biorxiv.org/content/10.1101/2021.01.07.425773v1</a></p> <p>This code was tested on Matlab v2020a and v2021a using commercially available laptop computers running the Windows 10 operating system.</p>