A geometric flow for segmenting vasculature in proton-density weighted MRI

Modern neurosurgery takes advantage of magnetic resonance images (MRI) of a patient’s cerebral anatomy and vasculature for planning before surgery and guidance during the procedure. Dual echo acquisitions are often performed that yield proton-density (PD) and T2-weighted images to evaluate edema nea...

Full description

Saved in:
Bibliographic Details
Published in:Medical image analysis Vol. 12; no. 4; pp. 497 - 513
Main Authors: Descoteaux, Maxime, Collins, D. Louis, Siddiqi, Kaleem
Format: Journal Article
Language:English
Published: Netherlands Elsevier B.V 01.08.2008
Subjects:
Algorithms
Brain - anatomy & histology
Brain Neoplasms - diagnosis
Brain Neoplasms - surgery
Cerebrovascular Circulation
Geometric flows
Humans
Magnetic Resonance Angiography - methods
Magnetic Resonance Imaging - methods
Phantoms, Imaging
Proton-density MRI
Protons
Validation
Vessel segmentation
Validation
Vessel segmentation
Proton-density MRI
Geometric flows
ISSN:1361-8415, 1361-8423, 1361-8423
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Modern neurosurgery takes advantage of magnetic resonance images (MRI) of a patient’s cerebral anatomy and vasculature for planning before surgery and guidance during the procedure. Dual echo acquisitions are often performed that yield proton-density (PD) and T2-weighted images to evaluate edema near a tumor or lesion. In this paper we develop a novel geometric flow for segmenting vasculature in PD images, which can also be applied to the easier cases of MR angiography data or Gadolinium enhanced MRI. Obtaining vasculature from PD data is of clinical interest since the acquisition of such images is widespread, the scanning process is non-invasive, and the availability of vessel segmentation methods could obviate the need for an additional angiographic or contrast-based sequence during preoperative imaging. The key idea is to first apply Frangi’s vesselness measure [Frangi, A., Niessen, W., Vincken, K.L., Viergever, M.A., 1998. Multiscale vessel enhancement filtering. In: International Conference on Medical Image Computing and Computer Assisted Intervention, vol. 1496 of Lecture Notes in Computer Science, pp. 130–137] to find putative centerlines of tubular structures along with their estimated radii. This measure is then distributed to create a vector field which allows the flux maximizing flow algorithm of Vasilevskiy and Siddiqi [Vasilevskiy, A., Siddiqi, K., 2002. Flux maximizing geometric flows. IEEE Transactions on Pattern Analysis and Machine Intelligence 24 (12), 1565–1578] to be applied to recover vessel boundaries. We carry out a qualitative validation of the approach on PD, MR angiography and Gadolinium enhanced MRI volumes and suggest a new way to visualize the segmentations in 2D with masked projections. We validate the approach quantitatively on a single-subject data set consisting of PD, phase contrast (PC) angiography and time of flight (TOF) angiography volumes, with an expert segmented version of the TOF volume viewed as the ground truth. We then validate the approach quantitatively on 19 PD data sets from a new digital brain phantom, with semi-automatically obtained labels from the corresponding angiography volumes viewed as ground truth. A significant finding is that both for the single-subject and multi-subject studies, 90% or more of the vasculature in the ground truth segmentation is recovered from the automatic segmentation of the other volumes.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ObjectType-Article-1
ObjectType-Feature-2
ISSN:1361-8415
1361-8423
1361-8423
DOI:10.1016/j.media.2008.02.003