Fully automated attenuation measurement and motion correction in FLIP image sequences

TitleFully automated attenuation measurement and motion correction in FLIP image sequences
Publication TypeJournal Article
Year of Publication2012
Authorsvan de Giessen, M, van der Laan, AMA, Hendriks, EA, Vidorreta Diaz de Cerio, M, Reiber, JHC, Jost, CR, Tanke, HJ, Lelieveldt, BPF
Refereed DesignationRefereed
JournalIEEE transactions on medical imaging
Start Page461
Date Published2011 Oct 13

Fluorescence loss in photobleaching (FLIP) is a method to study compartment connectivity in living cells. A FLIP sequence is obtained by alternatively bleaching a spot in a cell and acquiring an image of the complete cell. Connectivity is estimated by comparing fluorescence signal attenuation in different cell parts. The measurements of the fluorescence attenuation are hampered by the low signal to noise ratio of the FLIP sequences, by sudden sample shifts and by sample drift. This paper describes a method that estimates the attenuation by modeling photobleaching as exponentially decaying signals. Sudden motion artifacts are minimized by registering the frames of a FLIP sequence to target frames based on the estimated model and by removing frames that contain deformations. Linear motion (sample drift) is reduced by minimizing the entropy of the estimated attenuation coefficients. Experiments on 16 in vivo FLIP sequences of muscle cells in Drosophila show that the proposed method results in fluorescence attenuations similar to the manually identified gold standard, but with standard deviations of approximately 50 times smaller. As a result of this higher precision, cell compartment edges and details such as cell nuclei become clearly discernible. The main value of this method is that it uses a model of the bleaching process to correct motion and that the model based fluorescence intensity and attenuation estimates can be interpreted easily. The proposed method is fully automatic, and runs in approximately one minute per sequence, making it suitable for unsupervised batch processing of large data series.

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PubMed ID21997250