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Online database of clinical MR and ultrasound images of brain tumors

Online database of clinical MR and ultrasound images of brain tumors Purpose: One of the important challenges in the field of medical imaging is finding real clinical images with which to validate new image processing algorithms. This is particularly true for tracked 3D ultrasound images of the brain. Methods: In 2010, pre- and postoperative magnetic resonance and intraoperative ultrasound images were acquired from brain tumor patients involved in the authors’ imaging study at the Montreal Neurological Institute. Results: These data are available online at the Montreal Neurological Institute’s Brain Images of Tumors for Evaluation database, termed here the MNI BITE database. It contains ultrasound and magnetic resonance images from 14 patients. Each patient underwent a preoperative and a postoperative T1-weighted magnetic resonance scan with gadolinium enhancement, and multiple intraoperative B-mode images were acquired before and after resection. Corresponding features were manually selected in some image pairs for validation. All images are in MINC format, the file format used at the authors’ institute for image processing. The MINC tools are available for free download at packages.bic.mni.mcgill.ca. Conclusions: This is the first online database of its kind. These images can be used by image processing scientists as well as clinicians wishing to compare findings from magnetic resonance and ultrasound imaging. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Medical Physics American Association of Physicists in Medicine

Online database of clinical MR and ultrasound images of brain tumors

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References (34)

Publisher
American Association of Physicists in Medicine
Copyright
Copyright © 2012 American Association of Physicists in Medicine
ISSN
0094-2405
DOI
10.1118/1.4709600
pmid
22755708
Publisher site
See Article on Publisher Site

Abstract

Purpose: One of the important challenges in the field of medical imaging is finding real clinical images with which to validate new image processing algorithms. This is particularly true for tracked 3D ultrasound images of the brain. Methods: In 2010, pre- and postoperative magnetic resonance and intraoperative ultrasound images were acquired from brain tumor patients involved in the authors’ imaging study at the Montreal Neurological Institute. Results: These data are available online at the Montreal Neurological Institute’s Brain Images of Tumors for Evaluation database, termed here the MNI BITE database. It contains ultrasound and magnetic resonance images from 14 patients. Each patient underwent a preoperative and a postoperative T1-weighted magnetic resonance scan with gadolinium enhancement, and multiple intraoperative B-mode images were acquired before and after resection. Corresponding features were manually selected in some image pairs for validation. All images are in MINC format, the file format used at the authors’ institute for image processing. The MINC tools are available for free download at packages.bic.mni.mcgill.ca. Conclusions: This is the first online database of its kind. These images can be used by image processing scientists as well as clinicians wishing to compare findings from magnetic resonance and ultrasound imaging.

Journal

Medical PhysicsAmerican Association of Physicists in Medicine

Published: Jun 1, 2012

Keywords: biomedical MRI; biomedical ultrasonics; brain; cancer; feature extraction; gadolinium; image enhancement; medical image processing; tumours; ultrasonic imaging; Web sites; online database; intraoperative ultrasound; MRI; brain tumors; validation; registration

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