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.
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 tumorpatients 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.
Authors: Inês Machado; Matthew Toews; Elizabeth George; Prashin Unadkat; Walid Essayed; Jie Luo; Pedro Teodoro; Herculano Carvalho; Jorge Martins; Polina Golland; Steve Pieper; Sarah Frisken; Alexandra Golby; William Wells Iii; Yangming Ou Journal: Neuroimage Date: 2019-08-22 Impact factor: 6.556
Authors: Simon Drouin; Anna Kochanowska; Marta Kersten-Oertel; Ian J Gerard; Rina Zelmann; Dante De Nigris; Silvain Bériault; Tal Arbel; Denis Sirhan; Abbas F Sadikot; Jeffery A Hall; David S Sinclair; Kevin Petrecca; Rolando F DelMaestro; D Louis Collins Journal: Int J Comput Assist Radiol Surg Date: 2016-08-31 Impact factor: 2.924
Authors: Nidiyare Hevia-Montiel; Pedro I Rodriguez-Perez; Paul J Lamothe-Molina; Alfonso Arellano-Reynoso; Ernesto Bribiesca; Marco A Alegria-Loyola Journal: J Med Imaging (Bellingham) Date: 2015-05-12
Authors: Inês Machado; Matthew Toews; Jie Luo; Prashin Unadkat; Walid Essayed; Elizabeth George; Pedro Teodoro; Herculano Carvalho; Jorge Martins; Polina Golland; Steve Pieper; Sarah Frisken; Alexandra Golby; William Wells Journal: Int J Comput Assist Radiol Surg Date: 2018-06-04 Impact factor: 2.924