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

Hsieh Fu Tsai, Joanna Gajda, Tyler F.W. Sloan, Andrei Rares, Amy Q. Shen

Research output: Contribution to journalArticle

16 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)230-237
Number of pages8
JournalSoftwareX
Volume9
DOIs
StatePublished - Jan 1 2019

Keywords

  • Convolutional neural network
  • Instance-aware segmentation
  • Machine learning
  • Phase contrast microscopy
  • Single-cell migration
  • Stain-free cell tracking

ASJC Scopus subject areas

  • Software
  • Computer Science Applications

Fingerprint Dive into the research topics of 'Usiigaci: Instance-aware cell tracking in stain-free phase contrast microscopy enabled by machine learning'. Together they form a unique fingerprint.

Cite this