Probabilistic atlas

Last revised by Daniel J Bell on 27 Sep 2019

Citation, DOI, disclosures and article data

Citation:

Moore C, Bell D, Probabilistic atlas. Reference article, Radiopaedia.org (Accessed on 15 Jun 2024) https://doi.org/10.53347/rID-71280

DOI:

https://doi.org/10.53347/rID-71280

Permalink:

https://radiopaedia.org/articles/71280

rID:

71280

Article created:

26 Sep 2019, Candace Makeda Moore

Disclosures:

At the time the article was created Candace Makeda Moore had no recorded disclosures.

View Candace Makeda Moore's current disclosures

Last revised:

27 Sep 2019, Daniel J Bell ◉

Disclosures:

At the time the article was last revised Daniel J Bell had no recorded disclosures.

View Daniel J Bell's current disclosures

Revisions:

2 times, by 2 contributors - see full revision history and disclosures

Sections:

Anatomy, Pathology

Synonyms:

Probability map
Probability atlases
Probability atlas

Probabilistic atlases, also known as probability maps, are anatomical or anatomopathological atlases based on statistically-weighted composites of many specimens. Traditional anatomy atlases were based on one or a few specimens sometimes with common variations indicated or shown. Probabilistic atlases are made through techniques that take data from many patients and combine it. Such atlases can be presented in ways as to show variation over populations and/or time. Because such atlases are produced from digital imaging they can be produced in such a way as to be queried for precise probability distributions of not only anatomy but useful functional variables such as blood flow velocity ^1,2. They have proved particularly useful for automating segmentation of both anatomy ^3,4and pathology ^5-7.

References

1. Mazziotta JC, Toga AW, Evans A, Fox P, Lancaster J. A probabilistic atlas of the human brain: theory and rationale for its development. The International Consortium for Brain Mapping (ICBM). (1995) NeuroImage. 2 (2): 89-101. Pubmed
2. Lee JS, Lee DS, Kim YK, Kim J, Lee HY, Lee SK, Chung JK, Lee MC. Probabilistic map of blood flow distribution in the brain from the internal carotid artery. (2004) NeuroImage. 23 (4): 1422-31. doi:10.1016/j.neuroimage.2004.07.057 - Pubmed
3. Ahmed Hosny, Chintan Parmar, John Quackenbush, Lawrence H. Schwartz, Hugo J. W. L. Aerts. Artificial intelligence in radiology. (2018) Nature Reviews Cancer. 18 (8): 500. doi:10.1038/s41568-018-0016-5 - Pubmed
4. Neeraj Sharma, Lalit M Aggarwal. Automated medical image segmentation techniques. (2010) Journal of Medical Physics. 35 (1): 3. doi:10.4103/0971-6203.58777 - Pubmed
5. Sarah Parisot, Amélie Darlix, Cédric Baumann, Sonia Zouaoui, Yordanka Yordanova, Marie Blonski, Valérie Rigau, Stéphane Chemouny, Luc Taillandier, Luc Bauchet, Hugues Duffau, Nikos Paragios. A Probabilistic Atlas of Diffuse WHO Grade II Glioma Locations in the Brain. (2016) PLOS ONE. 11 (1): e0144200. doi:10.1371/journal.pone.0144200 - Pubmed
6. Xiao Han, Mischa S. Hoogeman, Peter C. Levendag, Lyndon S. Hibbard, David N. Teguh, Peter Voet, Andrew C. Cowen, Theresa K. Wolf. Atlas-Based Auto-segmentation of Head and Neck CT Images. (2008) Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. 11 (Pt 2): 434. doi:10.1007/978-3-540-85990-1_52 - Pubmed
7. Carl Sjöberg, Martin Lundmark, Christoffer Granberg, Silvia Johansson, Anders Ahnesjö, Anders Montelius. Clinical evaluation of multi-atlas based segmentation of lymph node regions in head and neck and prostate cancer patients. (2013) Radiation Oncology. 8 (1): 1. doi:10.1186/1748-717X-8-229 - Pubmed