Large histological serial sections for computational tissue volume reconstruction. - Braumann, U. D.; Scherf, N.; Einenkel, J.; Horn, L. C.; Wentzensen, N.; Loeffler, M.; Kuska, J. P. (Methods Inf Med, 2007)
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.
Keywords: Biomarkers, Tumor; Cervix Uteri/anatomy & histology/*pathology; *Computational Biology; Feasibility Studies; Female; Histological Techniques; Humans; *Image Processing, Computer-Assisted; Models, Statistical; Statistics, Nonparametric; T-Lymphocytes; Uterine Cervical Diseases/*diagnosis/pathology