Evaluation of methods for detection of fluorescence labeled subcellular objects in microscope images

BMC Bioinformatics

Research Article

Olli Yli-Harja¹  Pekka Ruusuvuori¹  Jyrki Selinummi¹  Sharif Chowdhury¹  Tarmo Äijö¹  Cecilia Garmendia-Torres²  Aimée M Dudley²  Lucas Pelkmans³  Mirko Birbaumer³ 

[1] Department of Signal Processing, Tampere University of Technology, P.O.Box 553, 33101, Tampere, Finland;Institute for Systems Biology, 1441 N. 34th Street, 98103-8904, Seattle, WA, USA;Institute of Molecular Systems Biology, ETH Zürich, Wolfgang-Pauli-Str. 16, 8093, Zürich, Switzerland;

关键词: Kernel Density Estimation;    Simulated Image;    Spot Detection;    Reference Result;    Human Osteosarcoma Cell Line;   

DOI  :  10.1186/1471-2105-11-248

 received in 2009-09-17, accepted in 2010-05-13,  发布年份 2010

来源: Springer

【 摘 要 】

BackgroundSeveral algorithms have been proposed for detecting fluorescently labeled subcellular objects in microscope images. Many of these algorithms have been designed for specific tasks and validated with limited image data. But despite the potential of using extensive comparisons between algorithms to provide useful information to guide method selection and thus more accurate results, relatively few studies have been performed.ResultsTo better understand algorithm performance under different conditions, we have carried out a comparative study including eleven spot detection or segmentation algorithms from various application fields. We used microscope images from well plate experiments with a human osteosarcoma cell line and frames from image stacks of yeast cells in different focal planes. These experimentally derived images permit a comparison of method performance in realistic situations where the number of objects varies within image set. We also used simulated microscope images in order to compare the methods and validate them against a ground truth reference result. Our study finds major differences in the performance of different algorithms, in terms of both object counts and segmentation accuracies.ConclusionsThese results suggest that the selection of detection algorithms for image based screens should be done carefully and take into account different conditions, such as the possibility of acquiring empty images or images with very few spots. Our inclusion of methods that have not been used before in this context broadens the set of available detection methods and compares them against the current state-of-the-art methods for subcellular particle detection.

【 授权许可】

CC BY   
© Ruusuvuori et al; licensee BioMed Central Ltd. 2010