3D-reconstruction of basal cell carcinoma: a proof-of-principle study

  • Authors:

  • Patrick Scheibe;Tino Wetzig;Jens-Peer Kuska;Markus Löffler;Jan C. Simon;Uwe Paasch;Ulf-Dietrich Braumann

  • Affiliations:

  • Translational Centre for Regenerative Medicine, Universität Leipzig, Leipzig;Department of Dermatology, Venerology and Allergology, Universität Leipzig, Leipzig;Interdisciplinary Centre for Bioinformatics, Universität Leipzig, Leipzig, Germany;Institute for Medical Informatics, Statistics, and Epidemiology, Universität Leipzig, Leipzig;Department of Dermatology, Venerology and Allergology, Universität Leipzig, Leipzig;Department of Dermatology, Venerology and Allergology, Universität Leipzig, Leipzig;Interdisciplinary Centre for Bioinformatics, Universität Leipzig, Leipzig, Germany

  • Venue:
  • WBIR'10 Proceedings of the 4th international conference on Biomedical image registration
  • Year:
  • 2010

Quantified Score

Hi-index 0.00

Visualization

Abstract

This work presents a complete processing-chain for a 3D- reconstruction of Basal Cell Carcinoma (BCC). BCC is the most common malignant skin cancer with a high risk of local recurrence after insufficient treatment. Therefore, we have focused on the development of an automated image-processing chain for 3D-reconstruction of BCC using large histological serial sections. We introduce a novel kind of imageprocessing chain (core component: non-linear image registration) which is optimised for the diffuse nature of BCC. For full-automatic delineation of the tumour within the tissue we apply a fuzzy c-means segmentation method, which does not calculate a hard segmentation decision but class membership probabilities. This feature moves the binary decision tumorous vs. non-tumorous to the end of the processing chain, and it ensures smooth gradients which are needed for a consistent registration. We used a multi-grid form of the nonlinear registration effectively suppressing registration runs into local minima (possibly caused by diffuse nature of the tumour). To register the stack of images this method is applied in a new way to reduce a global drift of the image stack while registration. Our method was successfully applied in a proof-of-principle study for automated tissue volume reconstruction followed by a quantitative tumour growth analysis.