Several datasets are fostering innovation in higher-level functions for everyone, everywhere. By providing this repository, we hope to encourage the research community to focus on hard problems. In this repository, we present our medical imaging DICOM files of patients from our User Tests and Analysis 4 (UTA4) study. Here, we provide a dataset of the used medical images during the UTA4 tasks. This repository and respective dataset should be paired with the dataset-uta4-rates repository dataset. Work and results are published on a top Human-Computer Interaction (HCI) conference named AVI 2020 (page). Results were analyzed and interpreted on our Statistical Analysis charts. The user tests were made in clinical institutions, where clinicians diagnose several patients for a Single-Modality vs Multi-Modality comparison. For example, in these tests, we used both prototype-single-modality and prototype-multi-modality repositories for the comparison. On the same hand, the hereby dataset repres
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SR-Reg is a brain MR-CT registration dataset, deriving from SynthRAD 2023 (https://synthrad2023.grand-challenge.org/). This dataset contains 180 subjects preprocessed images, and each subject comprises a brain MR image and a brain CT image with corresponding segmentation label. SR-Reg is first introduced in MambaMorph (https://arxiv.org/abs/2401.13934).