Methods Inf Med 2007; 46(05): 614-622
DOI: 10.1160/ME9065
Paper
Schattauer GmbH

Large Histological Serial Sections for Computational Tissue Volume Reconstruction

U.-D. Braumann
1   Translational Centre for Regenerative Medicine, Leipzig, Germany
,
N. Scherf
1   Translational Centre for Regenerative Medicine, Leipzig, Germany
,
J. Einenkel
2   Department of Obstetrics and Gynecology, University of Leipzig, Leipzig, Germany
,
L.-C. Horn
3   Institute of Pathology, University of Leipzig, Leipzig, Germany
,
N. Wentzensen
4   Division for Applied Tumour Biology, Institute of Pathology, University of Heidelberg, Heidelberg, Germany
,
M. Loeffler
5   Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany
,
J.-P. Kuska
6   Interdisciplinary Centre for Bioinformatics, University of Leipzig, Leipzig, Germany
› Author Affiliations
Further Information

Publication History

Publication Date:
22 January 2018 (online)

Summary

Objectives: A proof of principle study was conducted for microscopic tissue volume reconstructions using a new image processing chain operating on alternately stained large histological serial sections.

Methods: Digital histological images were obtained from conventional brightfield transmitted light microscopy. A powerful nonparametric nonlinear optical flow-based registration approach was used. In order to apply a simple but computationally feasible sum-of-squared-differences similarity measure even in case of differing histological stainings, a new consistent tissue segmentation procedure was placed upstream.

Results: Two reconstructions from uterine cervix carcinoma specimen were accomplished, one alternately stained with p16INK4a (surrogate tumor marker) and H&E (routine reference), and another with three different alternate stainings, H&E, p16INK4a, and CD3 (a T-lymphocyte marker). For both cases, due to our segmentation-based reference-free nonlinear registration procedure, resulting tissue reconstructions exhibit utmost smooth image-to-image transitions without impairing warpings.

Conclusions: Our combination of modern nonparametric nonlinear registration and consistent tissue segmentation has turned out to provide a superior tissue reconstruction quality.

 
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