title: FiloDetect: automatic detection of filopodia from fluorescence microscopy images.
abstract: BACKGROUND
  Filopodia are small cellular projections that help cells to move through and sense their environment. Filopodia play crucial roles in processes such as development and wound-healing. Also, increases in filopodia number or size are characteristic of many invasive cancers and are correlated with increased rates of metastasis in mouse experiments. Thus, one possible route to developing anti-metastatic therapies is to target factors that influence the filopodia system. Filopodia can be detected by eye using confocal fluorescence microscopy, and they can be manually annotated in images to quantify filopodia parameters. Although this approach is accurate, it is slow, tedious and not entirely objective. Manual detection is a significant barrier to the discovery and quantification of new factors that influence the filopodia system.
  
  
  RESULTS
  Here, we present FiloDetect, an automated tool for detecting, counting and measuring the length of filopodia in fluorescence microscopy images. The method first segments the cell from the background, using a modified triangle threshold method, and then extracts the filopodia using a series of morphological operations. We verified the accuracy of FiloDetect on Rat2 and B16F1 cell images from three different labs, showing that per-cell filopodia counts and length estimates are highly correlated with the manual annotations. We then used FiloDetect to assess the role of a lipid kinase on filopodia production in breast cancer cells. Experimental results show that PI4KIII β expression leads to an increase in filopodia number and length, suggesting that PI4KIII β is involved in driving filopodia production.
  
  
  CONCLUSIONS
  FiloDetect provides accurate and objective quantification of filopodia in microscopy images, and will enable large scale comparative studies to assess the effects of different genetic and chemical perturbations on filopodia production in different cell types, including cancer cell lines.
authors.affiliation: Ottawa Hospital Research Institute, 501 Smyth Road, Ottawa, Ontario K1Y 4E9, Canada. snilufar@gmail.com
authors.forename: Sharmin
authors.initials: S
authors.lastname: Nilufar
authors.forename: Anne A
authors.initials: AA
authors.lastname: Morrow
authors.forename: Jonathan M
authors.initials: JM
authors.lastname: Lee
authors.forename: Theodore J
authors.initials: TJ
authors.lastname: Perkins
mesh_terms: D000465:Algorithms; D000818:Animals; D001331:Automation; D045744:Cell Line, Tumor; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D051379:Mice; D008856:Microscopy, Fluorescence / Q000379:methods*; D016501:Neurites / Q000378:metabolism; D011554:Pseudopodia / Q000378:metabolism*