Robust optical flow algorithm for general single cell segmentation

A self-supervised learning approach for high throughput and high content cell segmentation

In principle, ML/AI-based algorithms should enable rapid and accurate cell segmentation in high-throughput settings. However, reliance on large training datasets, human input, computational expertise, and limited generalizability has prevented this goal of completely automated, high-throughput segmentation from being achieved. To overcome these roadblocks, we introduce an innovative self-supervised learning method (SSL) for pixel classification that does not require parameter tuning or curated data sets, and instead trains itself on the end-users' own data in a completely automated fashion, thus providing a more efficient cell segmentation approach for high-throughput, high-content image analysis. We demonstrate that our algorithm meets the criteria of being fully automated with versatility across various magnifications, optical modalities, and cell types. Moreover, our SSL algorithm is capable of identifying complex cellular structures and organelles, which are otherwise easily missed, thereby broadening the machine learning applications to high-content imaging. Our SSL technique displayed consistently high F1 scores across segmented cell images, with scores ranging from 0.771 to 0.888, matching or outperforming the popular Cellpose algorithm, which showed a greater F1 variance of 0.454 to 0.882, primarily due to more false negatives.

Automated cell segmentation for reproducibility in bioimage analysis

Live-cell imaging is extremely common in synthetic biology research, but its ability to be applied reproducibly across laboratories can be hindered by a lack of standardized image analysis. Here, we introduce a novel cell segmentation method developed as part of a broader Independent Verification & Validation (IV&V) program aimed at characterizing engineered Dictyostelium cells. Standardizing image analysis was found to be highly challenging: the amount of human judgment required for parameter optimization, algorithm tweaking, training and data pre-processing steps forms serious challenges for reproducibility. To bring automation and help remove bias from live-cell image analysis, we developed a self-supervised learning (SSL) method that recursively trains itself directly from motion in live-cell microscopy images without any end-user input, thus providing objective cell segmentation. Here, we highlight this SSL method applied to characterizing the engineered Dictyostelium cells of the original IV&V program. This approach is highly generalizable, accepting images from any cell type or optical modality without the need for manual training or parameter optimization. This method represents an important step toward automated bioimage analysis software and reflects broader efforts to design accessible measurement technologies to enhance reproducibility in synthetic biology research.

Self-supervised machine learning for live cell imagery segmentation

Segmenting single cells is a necessary process for extracting quantitative data from biological microscopy imagery. The past decade has seen the advent of machine learning (ML) methods to aid in this process, the overwhelming majority of which fall under supervised learning (SL) which requires vast libraries of pre-processed, human-annotated labels to train the ML algorithms. Such SL pre-processing is labor intensive, can introduce bias, varies between end-users, and has yet to be shown capable of robust models to be effectively utilized throughout the greater cell biology community. Here, to address this pre-processing problem, we offer a self-supervised learning (SSL) approach that utilizes cellular motion between consecutive images to self-train a ML classifier, enabling cell and background segmentation without the need for adjustable parameters or curated imagery. By leveraging motion, we achieve accurate segmentation that trains itself directly on end-user data, is independent of optical modality, outperforms contemporary SL methods, and does so in a completely automated fashion-thus eliminating end-user variability and bias. To the best of our knowledge, this SSL algorithm represents a first of its kind effort and has appealing features that make it an ideal segmentation tool candidate for the broader cell biology research community.