Computational imaging modalities for multi-focal whole-slide imaging systems

Leon Van Der Graaff, Geert J.L.H. Van Leenders, Fanny Boyaval, Sjoerd Stallinga

Research output: Contribution to journalArticleScientificpeer-review

4 Citations (Scopus)
38 Downloads (Pure)

Abstract

Whole-slide imaging systems can generate full-color image data of tissue slides efficiently, which are needed for digital pathology applications. This paper focuses on a scanner architecture that is based on a multi-line image sensor that is tilted with respect to the optical axis, such that every line of the sensor scans the tissue slide at a different focus level. This scanner platform is designed for imaging with continuous autofocus and inherent color registration at a throughput of the order of 400 MPx/s. Here, single-scan multi-focal whole-slide imaging, enabled by this platform, is explored. In particular, two computational imaging modalities based on multi-focal image data are studied. First, 3D imaging of thick absorption stained slides (∼60 μm) is demonstrated in combination with deconvolution to ameliorate the inherently weak contrast in thick-Tissue imaging. Second, quantitative phase tomography is demonstrated on unstained tissue slides and on fluorescently stained slides, revealing morphological features com-plementary to features made visible with conventional absorption or fluorescence stains. For both computational approaches simplified algorithms are proposed, targeted for straightforward parallel processing implementation at ∼GPx=s throughputs.

Original languageEnglish
Pages (from-to)5967-5982
Number of pages16
JournalApplied Optics
Volume59
Issue number20
DOIs
Publication statusPublished - 2020

Bibliographical note

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

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