ScholarlyCommons: Repository

Title

Non-Rigid Registration between Histological and MR Images of the Prostate: A Joint Segmentation and Registration Framework

Author(s)

Yangming Ou, University of PennsylvaniaFollow
Dinggang Shen, University of PennsylvaniaFollow
Michael Feldman, University of PennsylvaniaFollow
John Tomaszewski, University of PennsylvaniaFollow
Christos Davatzikos, University of PennsylvaniaFollow

Document Type

Conference Paper

Date of this Version

6-2009

Comments

Yangming Ou, Dinggang Shen, Michael Feldman, John Tomaszewski, Christos Davatzikos. "Non-Rigid Registration between Histological and MR Images of the Prostate: A Joint Segmentation and Registration Framework". Computer Vision and Pattern Recognition (CVPR) Workshop: Mathematical Methods in Biomedical Image Analysis (MMBIA), Miami, FL, 2009: pp. 125-132.

http://www.seas.upenn.edu/~ouya/documents/research/Ou09_MMBIA.pdf

Abstract

This paper presents a 3D non-rigid registration algorithm between histological and MR images of the prostate with cancer. To compensate for the loss of 3D integrity in the histology sectioning process, series of 2D histological slices are first reconstructed into a 3D histological volume. After that, the 3D histology-MRI registration is obtained by maximizing a) landmark similarity and b) cancer region overlap between the two images. The former aims to capture distortions at prostate boundary and internal bloblike structures; and the latter aims to capture distortions specifically at cancer regions. In particular, landmark similarities, the former, is maximized by an annealing process, where correspondences between the automatically-detected boundary and internal landmarks are iteratively established in a fuzzy-to-deterministic fashion. Cancer region overlap, the latter, is maximized in a joint cancer segmentation and registration framework, where the two interleaved problems – segmentation and registration – inform each other in an iterative fashion. Registration accuracy is established by comparing against human-rater-defined landmarks and by comparing with other methods. The ultimate goal of this registration is to warp the histologically-defined cancer ground truth into MRI, for more thoroughly understanding MRI signal characteristics of the prostate cancerous tissue, which will promote the MRI-based prostate cancer diagnosis in the future studies.

Keywords

Image Registration, Histological Image, MR Image, Deformable Registration, Non-Rigid Registration