MR prior based automatic segmentation of the prostate in TRUS images for MR/TRUS data fusion

Authors:
Sébastien Martin;Michael Baumann;Vincent Daanen;Jocelyne Troccaz
Affiliations:
Université J. Fourier, TIMC laboratory, Grenoble, France and CNRS, UMR;Université J. Fourier, TIMC laboratory, Grenoble, France and CNRS, UMR and Koelis SAS, La Tronche, France;Koelis SAS, La Tronche, France;Université J. Fourier, TIMC laboratory, Grenoble, France and CNRS, UMR
Venue:
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
Year:
2010

Citing 1
Cited 3

Active shape models—their training and application

Computer Vision and Image Understanding

Realtime TRUS/MRI fusion targeted-biopsy for prostate cancer: a clinical demonstration of increased positive biopsy rates

MICCAI'10 Proceedings of the 2010 international conference on Prostate cancer imaging: computer-aided diagnosis, prognosis, and intervention
Editorial: Information fusion in the realm of medical applications - A bibliographic glimpse at its growing appeal

Information Fusion
Required accuracy of MR-US registration for prostate biopsies

MICCAI'11 Proceedings of the 2011 international conference on Prostate cancer imaging: image analysis and image-guided interventions

Quantified Score

Hi-index	0.00

Visualization

Abstract

The poor signal-to-noise ratio in transrectal ultrasound (TRUS) images makes the fully automatic segmentation of the prostate challenging and most approaches proposed in the literature still lack robustness and accuracy. However, it is relatively straightforward to obtain high quality segmentations in magnetic resonance (MR) images. In the context of MR to TRUS data fusion the information gathered in the MR images can hence provide a strong prior for US segmentation. In this paper, we describe a method to non-linearly register a patient specific mesh of the prostate build from MR images to TRUS volume. The MR prior provides shape and volume constraints that are used to guide the MR-to-TRUS surface deformation, in collaboration with a US image contour appearance model. The anatomical point correspondences between the MR and TRUS surfaces are obtained implicitly. The method was validated on 30 pairs of MRI-TRUS patient exams and achieves a mean Dice value 0.85 and a mean surface error of 2.0 mm.