Journal
SOFTWAREX
Volume 9, Issue -, Pages 230-237Publisher
ELSEVIER
DOI: 10.1016/j.softx.2019.02.007
Keywords
Phase contrast microscopy; Instance-aware segmentation; Machine learning; Convolutional neural network; Stain-free cell tracking; Single-cell migration
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Funding
- JSPS KAKENHI, Japan [JP1700362]
- Okinawa Institute of Science and Technology Graduate University (OIST), Japan
- Cabinet Office, Government of Japan
- Scientific Computing and Data Analysis Section of OIST Graduate University
- Community Relations Section of OIST Graduate University
- Imaging Analysis Section of OIST Graduate University
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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. (C) 2019 The Authors. Published by Elsevier B.V.
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