Large histological serial sections for computational tissue volume 
reconstruction

Braumann, U.-D.; Scherf, Nico; Einenkel, J.; Horn, L.-C.; Wentzensen, N.; Loeffler, M.; Kuska, J.-P.

doi:10.1160/ME9065

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Large histological serial sections for computational tissue volume reconstruction

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Citation

Braumann, U.-D., Scherf, N., Einenkel, J., Horn, L.-C., Wentzensen, N., Loeffler, M., et al. (2007). Large histological serial sections for computational tissue volume reconstruction. Methods of Information in Medicine, 46(5), 614-622. doi:10.1160/ME9065.

Cite as: https://hdl.handle.net/21.11116/0000-0007-B957-7

Abstract

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 p16(INK4a) (surrogate tumor marker) and H&E (routine reference), and another with three different alternate stainings, H&E, p16(INK4a), 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.