ArticleCytometry. Part A : the journal of the International Society for Analytical Cytology2013
A computer vision approach to rare cell in vivo fluorescence flow cytometry.
Article in Cytometry. Part A : the journal of the International Society for Analytical Cytology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 28 citations in OpenAlex.
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- Machine Learning Approach for Enumeration of Circulating Cells with DiffusebioRxiv : the preprint server for biology · 2026Article
- Considerations for the use of targeted fluorescence contrast agents to detect circulating cancer cell populations with diffuseJournal of biomedical optics · 2026Article
- Deep learning enabled label-free microfluidic droplet classification for single cell functional assays.Frontiers in bioengineering and biotechnology · 2024Article
- Image Analysis Using Machine Learning for Automated Detection of Hemoglobin H Inclusions in Blood Smears - A Method for Morphologic Detection of Rare Cells.Journal of pathology informatics · 2021Article
- Biomedical Applications of Tissue Clearing and Three-Dimensional Imaging in Health and Disease.iScience · 2020Review
- Flow-regulated endothelial glycocalyx determines metastatic cancer cell activity.FASEB journal : official publication of the Federation of American Societies for Experimental Biology · 2020Article
- Detection of Rare Objects by Flow Cytometry: Imaging, Cell Sorting, and Deep Learning Approaches.International journal of molecular sciences · 2020Review
- Phase contrast time-lapse microscopy datasets with automated and manual cell tracking annotations.Scientific data · 2018Article
- Fluorescence detection, enumeration and characterization of single circulating cells in vivo: technology, applications and future prospects.Physics in medicine and biology · 2017Article
- Diffuse fluorescence fiber probe for in vivo detection of circulating cells.Journal of biomedical optics · 2017Article
- Endogenous light scattering as an optical signature of circulating tumor cell clusters.Biomedical optics express · 2016Article
- Performance of computer vision in vivo flow cytometry with low fluorescence contrast.Journal of biomedical optics · 2015Article
- In vivo photoswitchable flow cytometry for direct tracking of single circulating tumor cells.Chemistry & biology · 2014Article
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
Abstract
Noninvasive enumeration of rare circulating cell populations in small animals is of great importance in many areas of biomedical research. In this work, we describe a macroscopic fluorescence imaging system and automated computer vision algorithm that allows in vivo detection, enumeration and tracking of circulating fluorescently-labeled cells from multiple large blood vessels in the ear of a mouse. This imaging system uses a 660 nm laser and a high sensitivity electron-multiplied charge coupled device camera (EMCCD) to acquire fluorescence image sequences from relatively large (∼5 × 5 mm(2) ) imaging areas. The primary technical challenge was developing an automated method for identifying and tracking rare cell events in image sequences with substantial autofluorescence and noise content. To achieve this, we developed a two-step image analysis algorithm that first identified cell candidates in individual frames, and then merged cell candidates into tracks by dynamic analysis of image sequences. The second step was critical since it allowed rejection of >97% of false positive cell counts. Overall, our computer vision IVFC (CV-IVFC) approach allows single-cell detection sensitivity at estimated concentrations of 20 cells/mL of peripheral blood. In addition to simple enumeration, the technique recovers the cell's trajectory, which in the future could be used to automatically identify, for example, in vivo homing and docking events.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.