Medical Image Registration

The Parkinson’s Progression Markers Initiative (PPMI) dataset originates from an observational clinical and longitudinal study comprising evaluations of people with Parkinson’s disease (PD), those people with high risk, and those who are healthy. Source: Time-Guided High-Order Attention Model of Longitudinal Heterogeneous Healthcare Data Image Source: https://www.ppmi-info.org/2013/08/imaging-inventory-whats-in-the-ppmi-database-2/

The goal of the Automated Cardiac Diagnosis Challenge (ACDC) challenge is to: - compare the performance of automatic methods on the segmentation of the left ventricular endocardium and epicardium as the right ventricular endocardium for both end diastolic and end systolic phase instances; - compare the performance of automatic methods for the classification of the examinations in five classes (normal case, heart failure with infarction, dilated cardiomyopathy, hypertrophic cardiomyopathy, abnormal right ventricle). The overall ACDC dataset was created from real clinical exams acquired at the University Hospital of Dijon. Acquired data were fully anonymized and handled within the regulations set by the local ethical committee of the Hospital of Dijon (France). Our dataset covers several well-defined pathologies with enough cases to (1) properly train machine learning methods and (2) clearly assess the variations of the main physiological parameters obtained from cine-MRI (in particular diastolic volume and ejection fraction). The dataset is composed of 150 exams (all from different patients) divided into 5 evenly distributed subgroups (4 pathological plus 1 healthy subject groups) as described below. Furthermore, each patient comes with the following additional information : weight, height, as well as the diastolic and systolic phase instants. The database is made available to participants through two datasets from the dedicated online evaluation website after a personal registration: i) a training dataset of 100 patients along with the corresponding manual references based on the analysis of one clinical expert; ii) a testing dataset composed of 50 new patients, without manual annotations but with the patient information given above. The raw input images are provided through the Nifti format. Source: Automated Cardiac Diagnosis Challenge Image source: Automated Cardiac Diagnosis Challenge

Learn2Reg is a dataset for medical image registration. Learn2Reg covers a wide range of anatomies (brain, abdomen, and thorax), modalities (ultrasound, CT, MR), availability of annotations, as well as intra- and inter-patient registration evaluation.

A dataset for single-image 3D in the wild consisting of annotations of detailed 3D geometry for 140,000 images. Source: OASIS: A Large-Scale Dataset for Single Image 3D in the Wild

IXI Dataset is a collection of 600 MR brain images from normal, healthy subjects. The MR image acquisition protocol for each subject includes: * T1, T2 and PD-weighted images * MRA images * Diffusion-weighted images (15 directions) The data has been collected at three different hospitals in London: * Hammersmith Hospital using a Philips 3T system (details of scanner parameters) * Guy’s Hospital using a Philips 1.5T system (details of scanner parameters) * Institute of Psychiatry using a GE 1.5T system (details of the scan parameters not available at the moment) The data has been collected as part of the project: * IXI – Information eXtraction from Images (EPSRC GR/S21533/02) The images in NIFTI format can be downloaded from here: This data is made available under the Creative Commons CC BY-SA 3.0 license. If you use the IXI data please acknowledge the source of the IXI data.

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).

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 represents the pieces of information of both BreastScreening and MIDA projects. These projects are research projects that deal with the use of a recently proposed technique in literature: Deep Convolutional Neural Networks (CNNs). From a developed User Interface (UI) and framework, these deep networks will incorporate several datasets in different modes. For more information about the available datasets please follow the Datasets page on the Wiki of the meta information repository. Last but not least, you can find further information on the Wiki in this repository. We also have several demos to see in our YouTube Channel, please follow us.

• The dataset contains full-spectral autofluorescence lifetime microscopic images (FS-FLIM) acquired on unstained ex-vivo human lung tissue, where 100 4D hypercubes of 256x256 (spatial resolution) x 32 (time bins) x 512 (spectral channels from 500nm to 780nm). This dataset associates with our paper "Deep Learning-Assisted Co-registration of Full-Spectral Autofluorescence Lifetime Microscopic Images with H&E-Stained Histology Images" (https://arxiv.org/abs/2202.07755) and "Full spectrum fluorescence lifetime imaging with 0.5 nm spectral and 50 ps temporal resolution" (https://doi.org/10.1038/s41467-021-26837-0). - The FS-FLIM images provide transformative insights into human lung cancer with extra-dimensional information. This will enable visual and precise detection of early lung cancer. With the methodology in our co-registration paper, FS-FLIM images can be registered with H&E-stained histology images, allowing characterisation of tumour and surrounding cells at a celluar level with absolute autofluorescence lifetime. - The dataset can be used for various purposes, including signal processing for optimal lifetime reconstruction, advanced image analysis for automatic feature extraction of lung cancer, and cellular-level characterisation of lung cancer with absolute label-free autofluorescence lifetime values. - The dataset is available on the University of Edinburgh's DataShare (https://doi.org/10.7488/ds/3099 and https://doi.org/10.7488/ds/3421)

Onchocerciasis is causing blindness in over half a million people in the world today. Drug development for the disease is crippled as there is no way of measuring effectiveness of the drug without an invasive procedure. Drug efficacy measurement through assessment of viability of onchocerca worms requires the patients to undergo nodulectomy which is invasive, expensive, time-consuming, skill-dependent, infrastructure dependent and lengthy process. The motivation of Niramai Oncho Dataset is to develop algorithms that can detect Onchocerca worms non-invasively using thermal imaging. The dataset consists of both thermal images and videos captured for each nodule site during imaging of a participant. Histopathological confirmation of the presence of female adult worm is used to obtain the ground truth. In total, the dataset consists of 125 participants' data with 192 palpable nodules. Out of 192 palpable nodules, 101 correspond to live female nodules and the remain 91 correspond to dead nodules.