Towards automated evaluation of mucus transport measured by microscopic OCT (mOCT) during hypertonic saline treatment of Cystic Fibrosis

H. Schulz-Hildebrandt, M. Pieper, J. Kasper, Nadine Spahr, Marcus A. Mall, P. König, G. Hüttmann


Effective mucus transport in the airways is essential for infection defense. Malfunction caused by diseases like cystic fibrosis (CF) can result in severe and even life threatening complications. Time resolved imaging of mucus transport in vivo is essential to get mechanistic insight in factors influencing the transport and for developing and testing therapeutic interventions to increase mucus transport.

Microscopic OCT (mOCT) was used successfully to image mucus transport in trachea of spontaneous breathing mice. Image series over more than 2 hours containing 35,000 frames were captured in wild type (WT) and βENaC overexpressing mice, which served as a model for CF. In order to evaluate these large amount of data an automatic quantitative evaluation is need. This has to include an efficient correction of tissue motion and an automatic identification and rejection of non-evaluable frames.

In this study we compared two algorithms for motion correction. A pairwise correlation of the different frame for calculation of the motion vector was matched to a maximization of the overlap of segmented images. Image series were evaluated with both algorithms and bench marked against a manual motion correction.

Due to the dominating speckle noise in the OCT images and tissue motion perpendicular to the plane of the cross-sectional images, the correlation algorithms was not able to correctly determine a correct tissue motion in all cases. Results of the optimization algorithms were more reliable after dedicated preprocessing of the images. In cases of strong motion both algorithms failed.

In conclusion, automatic motion correction of mOCT image series taken from mice trachea is possible. However, at the current stage manual supervision is still necessary.

Original languageEnglish
Issue number07
Pages (from-to)1-48
Number of pages48
Publication statusPublished - 2016

Research Areas and Centers

  • Academic Focus: Biomedical Engineering


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