{"id":189957,"identifier":"DKVPIJ","persistentUrl":"https://doi.org/10.18710/DKVPIJ","protocol":"doi","authority":"10.18710","separator":"/","publisher":"DataverseNO","publicationDate":"2023-11-06","storageIdentifier":"S3://10.18710/DKVPIJ","datasetType":"dataset","datasetVersion":{"id":3963,"datasetId":189957,"datasetPersistentId":"doi:10.18710/DKVPIJ","storageIdentifier":"S3://10.18710/DKVPIJ","versionNumber":1,"versionMinorNumber":0,"versionState":"RELEASED","latestVersionPublishingState":"RELEASED","deaccessionLink":"","lastUpdateTime":"2023-11-06T10:32:04Z","releaseTime":"2023-11-06T10:32:04Z","createTime":"2023-10-02T13:08:40Z","publicationDate":"2023-11-06","citationDate":"2023-11-06","license":{"name":"CC0 1.0","uri":"http://creativecommons.org/publicdomain/zero/1.0","iconUri":"https://licensebuttons.net/p/zero/1.0/88x31.png","rightsIdentifier":"CC0-1.0","rightsIdentifierScheme":"SPDX","schemeUri":"https://spdx.org/licenses/","languageCode":"en"},"fileAccessRequest":true,"metadataBlocks":{"citation":{"displayName":"Citation Metadata","name":"citation","fields":[{"typeName":"title","multiple":false,"typeClass":"primitive","value":"Deform-registered rectal contours for prostate cancer patients - 373 contours from 37 patients"},{"typeName":"author","multiple":true,"typeClass":"compound","value":[{"authorName":{"typeName":"authorName","multiple":false,"typeClass":"primitive","value":"Rørtveit, Øyvind Lunde"},"authorAffiliation":{"typeName":"authorAffiliation","multiple":false,"typeClass":"primitive","value":"University of Bergen"},"authorIdentifierScheme":{"typeName":"authorIdentifierScheme","multiple":false,"typeClass":"controlledVocabulary","value":"ORCID"},"authorIdentifier":{"typeName":"authorIdentifier","multiple":false,"typeClass":"primitive","value":"0000-0001-6545-663X"}}]},{"typeName":"datasetContact","multiple":true,"typeClass":"compound","value":[{"datasetContactName":{"typeName":"datasetContactName","multiple":false,"typeClass":"primitive","value":"Rørtveit, Øyvind Lunde"},"datasetContactAffiliation":{"typeName":"datasetContactAffiliation","multiple":false,"typeClass":"primitive","value":"University of Bergen"},"datasetContactEmail":{"typeName":"datasetContactEmail","multiple":false,"typeClass":"primitive","value":"oyvindlr@gmail.com"}}]},{"typeName":"dsDescription","multiple":true,"typeClass":"compound","value":[{"dsDescriptionValue":{"typeName":"dsDescriptionValue","multiple":false,"typeClass":"primitive","value":"Contours of the recti of 37 prostate cancer patients from Haukeland University Hospital years 2007-2009. The patients were part of a study on hypofractionated radiotherapy with integrated boost, as desribed in Ekanger et al. (2018). The data is now fully anonymous, as the identification keys have been deleted.\n\nEach patient had a planning CT plus 6-10 CT repeat CT scans taken during the course of radiotherapy. The rectum was delineated on every scan by an expert physicist. The contours are represented as polyhedra, where each shape contour consist of a set of  of 18063 points, with x, y and z-coordinates, on the organ surface. The points are tied together by 36168 triangular faces. The definition of of the triangular faces are given by an array of indices into the arrays of contour points. \nAll contours have been deformably registered to a reference shape; therefore, there is point-to-point correspondence between all shapes in the dataset. This makes the data ideal for statistical modelling.\n\nThis data was used in the models of Rørtveit et al. (2021) and Rørtveit et al. (2023). A description of the patients, treatment and imaging is given in Hysing et al. (2018). \n\nTwo representations of the data are available in this dataset; firstly a Matlab .mat file which contains all the data. For users who do not use Matlab, the data is also made available as text files in the form of comma separated values."},"dsDescriptionDate":{"typeName":"dsDescriptionDate","multiple":false,"typeClass":"primitive","value":"2023-10-02"}}]},{"typeName":"subject","multiple":true,"typeClass":"controlledVocabulary","value":["Medicine, Health and Life Sciences"]},{"typeName":"keyword","multiple":true,"typeClass":"compound","value":[{"keywordValue":{"typeName":"keywordValue","multiple":false,"typeClass":"primitive","value":"Radiotherapy"},"keywordVocabulary":{"typeName":"keywordVocabulary","multiple":false,"typeClass":"primitive","value":"[Mesh]"},"keywordVocabularyURI":{"typeName":"keywordVocabularyURI","multiple":false,"typeClass":"primitive","value":"https://www.ncbi.nlm.nih.gov/mesh/68011878"}},{"keywordValue":{"typeName":"keywordValue","multiple":false,"typeClass":"primitive","value":"Prostatic Neoplasms"},"keywordVocabulary":{"typeName":"keywordVocabulary","multiple":false,"typeClass":"primitive","value":"[Mesh]"},"keywordVocabularyURI":{"typeName":"keywordVocabularyURI","multiple":false,"typeClass":"primitive","value":"https://www.ncbi.nlm.nih.gov/mesh/?term=prostate+cancer"}},{"keywordValue":{"typeName":"keywordValue","multiple":false,"typeClass":"primitive","value":"Rectum"},"keywordVocabulary":{"typeName":"keywordVocabulary","multiple":false,"typeClass":"primitive","value":"[Mesh]"},"keywordVocabularyURI":{"typeName":"keywordVocabularyURI","multiple":false,"typeClass":"primitive","value":"https://www.ncbi.nlm.nih.gov/mesh/68012007"}},{"keywordValue":{"typeName":"keywordValue","multiple":false,"typeClass":"primitive","value":"Contours"}},{"keywordValue":{"typeName":"keywordValue","multiple":false,"typeClass":"primitive","value":"Deformable registration"}}]},{"typeName":"publication","multiple":true,"typeClass":"compound","value":[{"publicationCitation":{"typeName":"publicationCitation","multiple":false,"typeClass":"primitive","value":"Rørtveit ØL, Hysing LB, Stordal AS, Pilskog S. An organ deformation model using Bayesian inference to combine population and patient-specific data. Phys Med Biol. 2023;68(5):055009. doi:10.1088/1361-6560/acb8fc"},"publicationIDType":{"typeName":"publicationIDType","multiple":false,"typeClass":"controlledVocabulary","value":"doi"},"publicationIDNumber":{"typeName":"publicationIDNumber","multiple":false,"typeClass":"primitive","value":"10.1088/1361-6560/acb8fc"},"publicationURL":{"typeName":"publicationURL","multiple":false,"typeClass":"primitive","value":"https://dx.doi.org/10.1088/1361-6560/acb8fc"}},{"publicationCitation":{"typeName":"publicationCitation","multiple":false,"typeClass":"primitive","value":"Rørtveit ØL, Hysing LB, Stordal AS, Pilskog S. Reducing systematic errors due to deformation of organs at risk in radiotherapy. Medical Physics. 2021;48(11):6578-6587. doi:10.1002/mp.15262"},"publicationIDType":{"typeName":"publicationIDType","multiple":false,"typeClass":"controlledVocabulary","value":"doi"},"publicationIDNumber":{"typeName":"publicationIDNumber","multiple":false,"typeClass":"primitive","value":"10.1002/mp.15262"},"publicationURL":{"typeName":"publicationURL","multiple":false,"typeClass":"primitive","value":"https://onlinelibrary.wiley.com/doi/abs/10.1002/mp.15262"}},{"publicationCitation":{"typeName":"publicationCitation","multiple":false,"typeClass":"primitive","value":"Hysing LB, Ekanger C, Zolnay Á, et al. Statistical motion modelling for robust evaluation of clinically delivered accumulated dose distributions after curative radiotherapy of locally advanced prostate cancer. Radiother Oncol. 2018;128(2):327-335. doi:10.1016/j.radonc.2018.06.004"},"publicationIDType":{"typeName":"publicationIDType","multiple":false,"typeClass":"controlledVocabulary","value":"doi"},"publicationIDNumber":{"typeName":"publicationIDNumber","multiple":false,"typeClass":"primitive","value":"10.1016/j.radonc.2018.06.004"},"publicationURL":{"typeName":"publicationURL","multiple":false,"typeClass":"primitive","value":"https://dx.doi.org/10.1016/j.radonc.2018.06.004"}},{"publicationCitation":{"typeName":"publicationCitation","multiple":false,"typeClass":"primitive","value":"Ekanger, C., Helle, S.I., Heinrich, D., Johannessen, D.C., Karlsdóttir, Á., Nygård, Y., Halvorsen, O.J., Reisæter, L., Kvåle, R., Hysing, L.B., Dahl, O., 2020. Ten-Year Results From a Phase II Study on Image Guided, Intensity Modulated Radiation Therapy With Simultaneous Integrated Boost in High-Risk Prostate Cancer. Adv Radiat Oncol 5, 396–403. https://doi.org/10.1016/j.adro.2019.11.007"},"publicationIDType":{"typeName":"publicationIDType","multiple":false,"typeClass":"controlledVocabulary","value":"doi"},"publicationIDNumber":{"typeName":"publicationIDNumber","multiple":false,"typeClass":"primitive","value":"10.1016/j.adro.2019.11.007"},"publicationURL":{"typeName":"publicationURL","multiple":false,"typeClass":"primitive","value":"https://doi.org/10.1016/j.adro.2019.11.007"}}]},{"typeName":"productionPlace","multiple":true,"typeClass":"primitive","value":["Haukeland University Hospital, Bergen, Norway"]},{"typeName":"contributor","multiple":true,"typeClass":"compound","value":[{"contributorType":{"typeName":"contributorType","multiple":false,"typeClass":"controlledVocabulary","value":"Project Leader"},"contributorName":{"typeName":"contributorName","multiple":false,"typeClass":"primitive","value":"Hysing, Liv Bolstad"}},{"contributorType":{"typeName":"contributorType","multiple":false,"typeClass":"controlledVocabulary","value":"Researcher"},"contributorName":{"typeName":"contributorName","multiple":false,"typeClass":"primitive","value":"Pilskog, Sara"}},{"contributorType":{"typeName":"contributorType","multiple":false,"typeClass":"controlledVocabulary","value":"Researcher"},"contributorName":{"typeName":"contributorName","multiple":false,"typeClass":"primitive","value":"Ekanger, Christian"}},{"contributorType":{"typeName":"contributorType","multiple":false,"typeClass":"controlledVocabulary","value":"Related Person"},"contributorName":{"typeName":"contributorName","multiple":false,"typeClass":"primitive","value":"Thor, Maria"}}]},{"typeName":"distributor","multiple":true,"typeClass":"compound","value":[{"distributorName":{"typeName":"distributorName","multiple":false,"typeClass":"primitive","value":"University of Bergen"},"distributorAbbreviation":{"typeName":"distributorAbbreviation","multiple":false,"typeClass":"primitive","value":"UiB"},"distributorURL":{"typeName":"distributorURL","multiple":false,"typeClass":"primitive","value":"https://dataverse.no/dataverse/uib"}}]},{"typeName":"depositor","multiple":false,"typeClass":"primitive","value":"Rørtveit, Øyvind Lunde"},{"typeName":"dateOfDeposit","multiple":false,"typeClass":"primitive","value":"2023-10-02"},{"typeName":"dateOfCollection","multiple":true,"typeClass":"compound","value":[{"dateOfCollectionStart":{"typeName":"dateOfCollectionStart","multiple":false,"typeClass":"primitive","value":"2007-01-01"},"dateOfCollectionEnd":{"typeName":"dateOfCollectionEnd","multiple":false,"typeClass":"primitive","value":"2009-12-31"}}]},{"typeName":"kindOfData","multiple":true,"typeClass":"primitive","value":["Clinical data"]},{"typeName":"software","multiple":true,"typeClass":"compound","value":[{"softwareName":{"typeName":"softwareName","multiple":false,"typeClass":"primitive","value":"Matlab"},"softwareVersion":{"typeName":"softwareVersion","multiple":false,"typeClass":"primitive","value":"R2020a"}},{"softwareName":{"typeName":"softwareName","multiple":false,"typeClass":"primitive","value":"Matterhorn"},"softwareVersion":{"typeName":"softwareVersion","multiple":false,"typeClass":"primitive","value":"v.16"}}]}]},"geospatial":{"displayName":"Geospatial Metadata","name":"geospatial","fields":[]}},"files":[{"description":"Readme file. Contains documentation and metadata for the data set.","label":"00_readme.txt","restricted":false,"directoryLabel":"00_README.txt","version":2,"datasetVersionId":3963,"dataFile":{"id":190346,"persistentId":"doi:10.18710/DKVPIJ/LXAX2P","pidURL":"https://doi.org/10.18710/DKVPIJ/LXAX2P","filename":"00_readme.txt","contentType":"text/plain","friendlyType":"Plain Text","filesize":9089,"description":"Readme file. Contains documentation and metadata for the data set.","storageIdentifier":"S3://uit-dataverseno-prod01:18b4c831635-7688cf452c4c","rootDataFileId":-1,"md5":"6dca1e0c92ad45160eef6b3c89bf47d6","checksum":{"type":"MD5","value":"6dca1e0c92ad45160eef6b3c89bf47d6"},"tabularData":false,"creationDate":"2023-10-20","publicationDate":"2023-11-06","fileAccessRequest":true}},{"description":"A MATLAB(TM) data-file containing all data. \nContains the array \"faces\" which defines the polyhedron faces by referencing into the column numbers of the connected surface points. The struct array \"patientData\" contains all shapes for all patients. Each entry in \"patientData\" contains two fields, \"id\" which is a number that can be used to refer to individual patients, and a cell-array called contourPoints, which contains the seven to eleven rectum contour shapes for each patient. Each cell in the cell array is a 18063-by-3 matrix, where each row represents a point in 3D, and the columns represent x, y and z-coordinates, respectively. ","label":"patientdata.mat","restricted":false,"version":2,"datasetVersionId":3963,"dataFile":{"id":189959,"persistentId":"doi:10.18710/DKVPIJ/3JCJRP","pidURL":"https://doi.org/10.18710/DKVPIJ/3JCJRP","filename":"patientdata.mat","contentType":"application/matlab-mat","friendlyType":"MATLAB Data","filesize":151134058,"description":"A MATLAB(TM) data-file containing all data. \nContains the array \"faces\" which defines the polyhedron faces by referencing into the column numbers of the connected surface points. The struct array \"patientData\" contains all shapes for all patients. Each entry in \"patientData\" contains two fields, \"id\" which is a number that can be used to refer to individual patients, and a cell-array called contourPoints, which contains the seven to eleven rectum contour shapes for each patient. Each cell in the cell array is a 18063-by-3 matrix, where each row represents a point in 3D, and the columns represent x, y and z-coordinates, respectively. ","storageIdentifier":"S3://uit-dataverseno-prod01:18af079e4ed-ae17ced46f21","rootDataFileId":-1,"md5":"885234729410f15af1c4c79d11c0897f","checksum":{"type":"MD5","value":"885234729410f15af1c4c79d11c0897f"},"tabularData":false,"creationDate":"2023-10-02","publicationDate":"2023-11-06","fileAccessRequest":true}},{"description":"Zip archive containing all shapes stored as CSV-text files. Contains the same data as in the MATLAB file, but in text format (CSV, comma separated values).\nThe zip file contains the file \"polyhedron_faces.txt\" in the root folder. This file contains an array\ndefining the polyhedron faces. Each row in this array contains three indices (0-based) into the \nsurface point arrays, together, these three points define a triangular face. Furthermore, the zip archive contains 37 folders, one for each patient. The folders are named \"Patient <ID>\", where <ID> is a number used to refer to the patient. Each folder contains between seven and eleven csv files called \"shape <number>\", where <number> is between 1 and 11. The first shape (number 1) is from the planning CT; the remaining shapes are recorded during the course of radiotherapy. They are not guaranteed to be in chronological order. The csv files contain 18063 lines, each with three entries. The lines represent points in 3D, and three entries in each line represent x, y and z coordinates, respectively.","label":"Rectal_contours.zip","restricted":false,"version":2,"datasetVersionId":3963,"dataFile":{"id":189958,"persistentId":"doi:10.18710/DKVPIJ/MAZ6NH","pidURL":"https://doi.org/10.18710/DKVPIJ/MAZ6NH","filename":"Rectal_contours.zip","contentType":"application/zip","friendlyType":"ZIP Archive","filesize":166986184,"description":"Zip archive containing all shapes stored as CSV-text files. Contains the same data as in the MATLAB file, but in text format (CSV, comma separated values).\nThe zip file contains the file \"polyhedron_faces.txt\" in the root folder. This file contains an array\ndefining the polyhedron faces. Each row in this array contains three indices (0-based) into the \nsurface point arrays, together, these three points define a triangular face. Furthermore, the zip archive contains 37 folders, one for each patient. The folders are named \"Patient <ID>\", where <ID> is a number used to refer to the patient. Each folder contains between seven and eleven csv files called \"shape <number>\", where <number> is between 1 and 11. The first shape (number 1) is from the planning CT; the remaining shapes are recorded during the course of radiotherapy. They are not guaranteed to be in chronological order. The csv files contain 18063 lines, each with three entries. The lines represent points in 3D, and three entries in each line represent x, y and z coordinates, respectively.","storageIdentifier":"S3://uit-dataverseno-prod01:18af07d86b4-a76b0f287e2c","rootDataFileId":-1,"md5":"0b5a41cb02c95410242ac2af15eccb1f","checksum":{"type":"MD5","value":"0b5a41cb02c95410242ac2af15eccb1f"},"tabularData":false,"creationDate":"2023-10-02","publicationDate":"2023-11-06","fileAccessRequest":true}}],"citation":"Rørtveit, Øyvind Lunde, 2023, \"Deform-registered rectal contours for prostate cancer patients - 373 contours from 37 patients\", https://doi.org/10.18710/DKVPIJ, DataverseNO, V1"}}