Large histological serial sections for computational tissue volume reconstruction.
OBJECTIVE
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.
Braumann
U-D
UD
University of Leipzig, Translational Centre for Regenerative Medicine, Department of Obstetrics and Gynecology, Philipp-Rosenthal-Strasse 55, 04103 Leipzig, Germany. braumann@uni-leipzig.de
Scherf
N
N
Einenkel
J
J
Horn
L-C
LC
Wentzensen
N
N
Loeffler
M
M
Kuska
J-P
JP
D014408:Biomarkers, Tumor; D002584:Cervix Uteri / Q000033:anatomy & histology / Q000473:pathology*; D019295:Computational Biology; D005240:Feasibility Studies; D005260:Female; D006652:Histological Techniques; D006801:Humans; D007091:Image Processing, Computer-Assisted; D015233:Models, Statistical; D018709:Statistics, Nonparametric; D013601:T-Lymphocytes; D002577:Uterine Cervical Diseases / Q000175:diagnosis* / Q000473:pathology
D014408:Biomarkers, Tumor