https://researchdata.se/sv/catalogue Search results sv Radiotherapy treatment for prostate cancer relies on computed tomography (CT) and/or magnetic resonance imaging (MRI) for delineation of radiation targets and organs at risk (OARs). Manual delineation of these volumes is regarded as the gold standard for ground truth in machine learning applications but to acquire such data is tedious and time-consuming. A publicly available clinical dataset is introduced comprising MRI images, synthetic CT (sCT) images, target and OARs delineations, and radiotherapy dose distributions for 432 prostate cancer patients treated with an MRI-only radiotherapy workflow. An extended dataset with 35 patients is also included, containing the data mentioned above together with deep learning (DL)-generated delineations, DL uncertainty maps, and DL structures manually edited by four radiation oncologists. The publication of these resources aims to aid research within the fields of automated radiotherapy planning and structure delineation, inter-observer analyses, and DL uncertainty investigation. Wed, 15 Jan 2025 00:00:00 GMT https://researchdata.se/sv/catalogue/dataset/doi-10-23698-aida-lund-probe https://researchdata.se/sv/catalogue/dataset/doi-10-23698-aida-lund-probe AIDA Data Hub Rogowski, Viktor Olsson, Lars E Scherman, Jonas Persson, Emilia Kadhim, Mustafa Wetterstedt, Sacha Af Adalsteinn Gunnlaugsson Nilsson, Martin Nandor Vass Moreau, Mathieu Medhin, Maria Gebre Bäck, Sven Rosenschöld, Per Munck Af Engelholm, Silke Gustafsson, Christian Jamtheim MR-images of the prostate region from healthy volunteers acquired at Elekta unity MR-Linac at Uppsala University Hospital. Data from each volunteer consist of an initial T2-weighted scan, followed by a number of groups of paired low and high resolution data approximately 5 minutes apart with a 3D balanced steady state free precession sequence. The initial T2-image and all high resolution images are segmented by a single observer including prostate, bladder and rectum. Mon, 23 Jan 2023 00:00:00 GMT https://researchdata.se/sv/catalogue/dataset/doi-10-23698-aida-lesser https://researchdata.se/sv/catalogue/dataset/doi-10-23698-aida-lesser AIDA Data Hub Fransson, Samuel Strand, Robin Multi-echo gradient echo (MEGRE) MRI data with 8 different echo times (2.38-23.6 ms) from 326 + 40 prostate cancer patients with gold fiducial markers inserted into the prostate (train/validation + test dataset). A scientific paper that utilizes this dataset for deep learning has been [published](https://doi.org/10.1088/1361-6560/abb0f9). Underlying description of the technique and its first use has been described in a [previous publication](https://www.ncbi.nlm.nih.gov/pubmed/28803447). The data contains an image volume for each patient, for each echo time. The center of mass from the three inserted prostate gold fiducial markers was manually defined. The ground truth label for this dataset consist of spherical objects with a radius of 1-12 mm, inserted in the center of mass defined locations. Method in the paper uses 9 mm radius. Thu, 30 Apr 2020 00:00:00 GMT https://researchdata.se/sv/catalogue/dataset/doi-10-23698-aida-megre https://researchdata.se/sv/catalogue/dataset/doi-10-23698-aida-megre AIDA Data Hub Gustafsson, Christian Jamtheim