Usiigaci: Instance-aware cell tracking in stain-free phase contrast microscopy enabled by machine learning

Highlights

• All-in-one segmentation, tracking, and data analysis software for cell migration in stain-free phase contrast microscopy environment.

• High accuracy instance-aware segmentation using mask regional convolutional neural network.

• A Python based tracking module with GUI for manual verification of segmentation and tracking results.

• Quantitative analysis of both position and morphological evolution of single cell migration.

Abstract

Stain-free, single-cell segmentation and tracking is tantamount to the holy grail of microscopic cell migration analysis. Phase contrast microscopy (PCM) images with cells at high density are notoriously difficult to segment accurately; thus, manual segmentation remains the de facto standard practice. In this work, we introduce Usiigaci, an all-in-one, semi-automated pipeline to segment, track, and visualize cell movement and morphological changes in PCM. Stain-free, instance-aware segmentation is accomplished using a mask regional convolutional neural network (Mask R-CNN). A Trackpy-based cell tracker with a graphical user interface is developed for cell tracking and data verification. The performance of Usiigaci is validated with electrotaxis of NIH/3T3 fibroblasts. Usiigaci provides highly accurate cell movement and morphological information for quantitative cell migration analysis.