Background and Illumination Correction for Time-Lapse Microscopy Data with Correlated Foreground

Peng T, Lamm L, Loeffler D, Ahmed N, Navab N, Schroeder T, Marr C (2020)

Publication Type: Conference contribution

Publication year: 2020

Journal

Lecture Notes in Computer Science Springer Verlag

Publisher: Springer Science and Business Media Deutschland GmbH

Book Volume: 12265 LNCS

Pages Range: 174-183

Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Event location: Lima, PER

ISBN: 9783030597214

DOI: 10.1007/978-3-030-59722-1_17

Abstract

Due to the inherent imperfections in the optical path, microscopy images, particularly fluorescence microscopy images, are often skewed by uneven illumination and hence have spurious intensity variation, also known as shading or vignetting effect. Besides spatial intensity inhomogeneity, time-lapse microscopy imaging further suffers from background variation in time, mostly due to photo-bleaching of the background medium. Moreover, the temporal background variation is often experiment-specific and hence cannot be easily corrected, in contrast to shading, where a prospective calibration method can be used. Existing retrospective illumination correction methods, ranging from simple multi-image averaging to sophisticated optimisation based methods such as CIDRE and BaSiC, all assume that the foreground of all images is uncorrelated between each other. However, this assumption is violated in e.g. long-term time-lapse microscopy imaging of adherent stem cells, in which a strong foreground correlation is observed from frame to frame. In this paper, we propose a new illumination and background correction method for time-lapse imaging, based on low-rank and sparse decomposition. We incorporate binary segmentation masks that inform the weighting scheme of our reweighted L1 norm minimisation about foreground vs background pixels in the image. This yields a better separation of the low-rank and sparse component, hence improving the estimation of illumination profiles. Experiments on both simulated and real time-lapse data demonstrate that our approach is superior to existing illumination correction methods and improves single cell quantification.

Involved external institutions

Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt (HMGU) / Helmholtz Munich DE Germany (DE) Eidgenössische Technische Hochschule Zürich (ETHZ) / Swiss Federal Institute of Technology in Zurich CH Switzerland (CH) Technische Universität München (TUM) DE Germany (DE)

How to cite

APA:

Peng, T., Lamm, L., Loeffler, D., Ahmed, N., Navab, N., Schroeder, T., & Marr, C. (2020). Background and Illumination Correction for Time-Lapse Microscopy Data with Correlated Foreground. In Anne L. Martel, Purang Abolmaesumi, Danail Stoyanov, Diana Mateus, Maria A. Zuluaga, S. Kevin Zhou, Daniel Racoceanu, Leo Joskowicz (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 174-183). Lima, PER: Springer Science and Business Media Deutschland GmbH.

MLA:

Peng, Tingying, et al. "Background and Illumination Correction for Time-Lapse Microscopy Data with Correlated Foreground." Proceedings of the 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020, Lima, PER Ed. Anne L. Martel, Purang Abolmaesumi, Danail Stoyanov, Diana Mateus, Maria A. Zuluaga, S. Kevin Zhou, Daniel Racoceanu, Leo Joskowicz, Springer Science and Business Media Deutschland GmbH, 2020. 174-183.

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