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MR and CT data with multiobserver delineations of organs in the pelvic area: Part of the Gold Atlas project
Umeå University, Faculty of Medicine, Department of Radiation Sciences.
Umeå University, Faculty of Medicine, Department of Radiation Sciences.
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2018 (English)In: Medical physics (Lancaster), ISSN 0094-2405, Vol. 45, no 3, p. 1295-1300Article in journal (Refereed) Published
Abstract [en]

Purpose: We describe a public dataset with MR and CT images of patients performed in the same position with both multiobserver and expert consensus delineations of relevant organs in the male pelvic region. The purpose was to provide means for training and validation of segmentation algorithms and methods to convert MR to CT like data, i.e., so called synthetic CT (sCT).

Acquisition and validation methods: T1- and T2-weighted MR images as well as CT data were collected for 19 patients at three different departments. Five experts delineated nine organs for each patient based on the T2-weighted MR images. An automatic method was used to fuse the delineations. Starting from each fused delineation, a consensus delineation was agreed upon by the five experts for each organ and patient. Segmentation overlap between user delineations with respect to the consensus delineations was measured to describe the spread of the collected data. Finally, an open-source software was used to create deformation vector fields describing the relation between MR and CT images to further increase the usability of the dataset.

Data format and usage notes: The dataset has been made publically available to be used for academic purposes, and can be accessed from . Potential applicationsThe dataset provides a useful source for training and validation of segmentation algorithms as well as methods to convert MR to CT-like data (sCT). To give some examples: The T2-weighted MR images with their consensus delineations can directly be used as a template in an existing atlas-based segmentation engine; the expert delineations are useful to validate the performance of a segmentation algorithm as they provide a way to measure variability among users which can be compared with the result of an automatic segmentation; and the pairwise deformably registered MR and CT images can be a source for an atlas-based sCT algorithm or for validation of sCT algorithm.

Place, publisher, year, edition, pages
John Wiley & Sons, 2018. Vol. 45, no 3, p. 1295-1300
Keywords [en]
CT, MRI, open dataset, organs at risk, radiotherapy
National Category
Medical Image Processing
Identifiers
URN: urn:nbn:se:umu:diva-146581DOI: 10.1002/mp.12748ISI: 000427129700032PubMedID: 29322528OAI: oai:DiVA.org:umu-146581DiVA, id: diva2:1224076
Available from: 2018-06-26 Created: 2018-06-26 Last updated: 2018-06-26Bibliographically approved

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Nyholm, TufveJonsson, JoakimSöderström, KarinBlomqvist, LennartZackrisson, Björn
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