Large histological serial sections for computational tissue volume reconstruction

Methods Inf Med. 2007;46(5):614-22. doi: 10.1160/me9065.


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.

MeSH terms

  • Biomarkers, Tumor
  • Cervix Uteri / anatomy & histology
  • Cervix Uteri / pathology*
  • Computational Biology*
  • Feasibility Studies
  • Female
  • Histological Techniques
  • Humans
  • Image Processing, Computer-Assisted*
  • Models, Statistical
  • Statistics, Nonparametric
  • T-Lymphocytes
  • Uterine Cervical Diseases / diagnosis*
  • Uterine Cervical Diseases / pathology


  • Biomarkers, Tumor