TY - GEN
T1 - A Publicly Available, High Resolution, Unbiased CT Brain Template
AU - Muschelli, John
N1 - Funding Information:
Acknowledgments. This work has been supported by the R01NS060910 and 5U01NS080824 grants from the National Institute of Neurological Disorders and Stroke at the National Institutes of Health (NINDS/NIH).
Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
PY - 2020
Y1 - 2020
N2 - Clinical imaging relies heavily on X-ray computed tomography (CT) scans for diagnosis and prognosis. Many research applications aim to perform population-level analyses, which require images to be put in the same space, usually defined by a population average, also known as a template. We present an open-source, publicly available, high-resolution CT template. With this template, we provide voxel-wise standard deviation and median images, a basic segmentation of the cerebrospinal fluid spaces, including the ventricles, and a coarse whole brain labeling. This template can be used for spatial normalization of CT scans and research applications, including deep learning. The template was created using an anatomically-unbiased template creation procedure, but is still limited by the population it was derived from, an open CT data set without demographic information. The template and derived images are available at https://github.com/muschellij2/high_res_ct_template.
AB - Clinical imaging relies heavily on X-ray computed tomography (CT) scans for diagnosis and prognosis. Many research applications aim to perform population-level analyses, which require images to be put in the same space, usually defined by a population average, also known as a template. We present an open-source, publicly available, high-resolution CT template. With this template, we provide voxel-wise standard deviation and median images, a basic segmentation of the cerebrospinal fluid spaces, including the ventricles, and a coarse whole brain labeling. This template can be used for spatial normalization of CT scans and research applications, including deep learning. The template was created using an anatomically-unbiased template creation procedure, but is still limited by the population it was derived from, an open CT data set without demographic information. The template and derived images are available at https://github.com/muschellij2/high_res_ct_template.
KW - Brain template
KW - CT imaging
KW - CT template
KW - Computed tomography
UR - http://www.scopus.com/inward/record.url?scp=85086239398&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85086239398&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-50153-2_27
DO - 10.1007/978-3-030-50153-2_27
M3 - Conference contribution
AN - SCOPUS:85086239398
SN - 9783030501525
T3 - Communications in Computer and Information Science
SP - 358
EP - 366
BT - Information Processing and Management of Uncertainty in Knowledge-Based Systems - 18th International Conference, IPMU 2020, Proceedings
A2 - Lesot, Marie-Jeanne
A2 - Vieira, Susana
A2 - Reformat, Marek Z.
A2 - Carvalho, João Paulo
A2 - Wilbik, Anna
A2 - Bouchon-Meunier, Bernadette
A2 - Yager, Ronald R.
PB - Springer
T2 - 18th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2020
Y2 - 15 June 2020 through 19 June 2020
ER -