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Proceedings Paper

Multimodal image registration of ex vivo 4 Tesla MRI with whole mount histology for prostate cancer detection
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Paper Abstract

In this paper we present novel methods for registration and subsequent evaluation of whole mount prostate histological sections to corresponding 4 Tesla ex vivo magnetic resonance imaging (MRI) slices to complement our existing computer-aided diagnosis (CAD) system for detection of prostatic adenocarcinoma from high resolution MRI. The CAD system is trained using voxels labeled as cancer on MRI by experts who visually aligned histology with MRI. To address voxel labeling errors on account of manual alignment and delineation, we have developed a registration method called combined feature ensemble mutual information (COFEMI) to automatically map spatial extent of prostate cancer from histology onto corresponding MRI for prostatectomy specimens. Our method improves over intensity-based similarity metrics (mutual information) by incorporating unique information from feature spaces that are relatively robust to intensity artifacts and which accentuate the structural details in the target and template images to be registered. Our registration algorithm accounts for linear gland deformations in the histological sections resulting from gland fixing and serial sectioning. Following automatic registration of MRI and histology, cancer extent from histological sections are mapped to the corresponding registered MRI slices. The manually delineated cancer areas on MRI obtained via manual alignment of histological sections and MRI are compared with corresponding cancer extent obtained via COFEMI by a novel registration evaluation technique based on use of non-linear dimensionality reduction (locally linear embedding (LLE)). The cancer map on MRI determined by COFEMI was found to be significantly more accurate compared to the manually determined cancer mask. The performance of COFEMI was also found to be superior compared to image intensity-based mutual information registration.

Paper Details

Date Published: 7 March 2007
PDF: 12 pages
Proc. SPIE 6512, Medical Imaging 2007: Image Processing, 65121S (7 March 2007); doi: 10.1117/12.710558
Show Author Affiliations
Jonathan Chappelow, Rutgers Univ. (United States)
Anant Madabhushi, Rutgers Univ. (United States)
Mark Rosen, Univ. of Pennsylvania (United States)
John Tomaszeweski, Univ. of Pennsylvania (United States)
Michael Feldman, Univ. of Pennsylvania (United States)

Published in SPIE Proceedings Vol. 6512:
Medical Imaging 2007: Image Processing
Josien P. W. Pluim; Joseph M. Reinhardt, Editor(s)

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