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CTA Hypoperfusion Analysis
Upload your CT angiography brain to automatically compute the hypoperfused area relevant from stroke care. This would enable endovascular stroke therapy in stroke centers where advanced imaging is not available. A video showing how to use this Web App is available here. We have developed a point and click pipeline to perform the NIFTI format conversion, automatic registration and anonymization. It is available here. A video showing how to use the pipeline works is available here.

Associated publication: Giancardo L, Niktabe A, Ocasio L, Abdelkhaleq R, Salazar-Marioni S, Sheth SA. Segmentation of acute stroke infarct core using image-level labels on CT-angiography. NeuroImage: Clinical 2023;37:103362.


OCT-A for Acute Stroke
This Webapp allows to estimate the likelihood of acute stroke using the as input vessel density variables from OCT-A. It has been developed at the Giancardo lab at UTHealth as part of an effort to develop systems and data for identifying acute strokes using retina imaging. This would enable the deployment of life-saving drugs in situations where brain scans are typically not available such as ambulances or even space missions.

Associated publication: S. Pachade, I. Coronado, R. Abdelkhaleq, J. Yan, S. Salazar-Marioni, A. Jagolino, M. Bahrainian, R. Channa, S. A. Sheth, L. Giancardo. "Detection of Stroke with Retinal Microvascular Density and Self-Supervised Learning Using OCT-A and Fundus Imaging " J. Clin. Med. 2022, 11(24), 7408;



For freely accessible (for research purposes) tools please check our Gitlab and GitHub pages.


All these datasets are intended to be used uniquely for non-commercial research purposes and they require that the relevant publications indicated to be cited.

IACTA-EST Data Challenge
The Image Analysis for CTA Endovascular Stroke Therapy (IACTA-EST) Data Challenge. Large vessel occlusion (LVO) denotes the obstruction of large, proximal cerebral arteries and accounts for 24-46% of acute ischemic stroke. Brain CT-Angiography (CTA) is an imaging modality available in most hospitals, which is typically used to identify LVO. A quick identification is essential to enable endovascular-stroke-therapy (EST) a life-saving treatment. While commercial solutions exist, no comparative tests on a common dataset have been performed. We aim to bridge this gap with the first task of the IACTA-EST by providing a curated imaging dataset from multiple clinical sites with evaluation metrics. Additionally, the IACTA-EST challenge will evaluate the participants' ability to predict the success of EST by combining CTA and clinical variables.

Go to challenge website

PleThora - segmentations in diseased chest CTs
Automated or semi-automated algorithms intended for chest CT analyses typically require the creation of a 3D map of the thoracic volume as their initial step. Identifying this anatomic region precedes fundamental tasks such as lung structure segmentation, lesion detection, and radiomics feature extraction in analysis pipelines. However, automatic approaches to segment the thoracic volume maps struggle to perform consistently in subjects with diseased lungs – yet this is exactly the circumstance for which pipeline analyses would be most useful.To address this need, we have created PleThora, a dataset of pleural effusion and thoracic cavity segmentations in subjects with diseased lungs. PleThora consists of left and right thoracic cavity segmentations delineated on 402 CT scans from The Cancer Imaging Archive NSCLC Radiomics collection as well as separate segmentations labeling pleural effusions alone.

Associated publication: K J Kiser, S Ahmed, S Stieb, A S R Mohamed, H Elhalawani, P Y S Park, N S Doyle, B J Wang, A Barman, Z Li, W J Zheng, C D Fuller, and L Giancardo, “PleThora: Pleural effusion and thoracic cavity segmentations in diseased lungs for benchmarking chest CT processing pipelines,” Medical Physics, vol. 47, no. 11, pp. 5941–5952, (2020).
Download from Cancer Imaging Archive

HEI-MED - retina fundus dataset
The Hamilton Eye Institute Macular Edema Dataset (HEI-MED) (formerly DMED) is a collection of 169 fundus images to train and test image processing algorithms for the detection of exudates and diabetic macular edema. The images have been collected as part of a telemedicine network for the diagnosis of diabetic retinopathy developed by the Hamilton Eye Institute, the Image Science and Machine Vision Group at ORNL with the collaboration of the Université de Bourgogne. The dataset collection and cleaning was completed in 2010.

Associated publication: L Giancardo, F Meriaudeau, T P Karnowski, Y Li, S Gaarg, K W Tobin and E Chaum, “Exudate-based Diabetic Macular Edema Detection in Fundus Images Using Publicly Available Datasets”, Medical Image Analysis 16(1), 216—226, (2012).
Download from GitHub

MIT-SI - Sleep Inertia Digital Device Interaction Datasets
Data indicating that the routine interaction with computer keyboards can be used to detect the psychomotor impairment induced by sleep inertia. This dataset was developed during the PI's work at the Massachusetts Institute of Technology.


MIT-CS1PD/MIT-CS2PD Digital Device Interaction Datasets
Data indicating that the routine interaction with computer keyboards can be used to detect motor signs in the early stages of Parkinson's Disease (PD). We explore a solution that measures the key hold times (the time required to press and release a key) during the normal use of a computer without any change in hardware and converts it to a PD motor index. This dataset was developed during the PI's work at the Massachusetts Institute of Technology.

Associated publication: L Giancardo, A Sanchez-Ferro, I Butterworth, C Sanchez-Mendoza and J M Hooker, “Psychomotor Impairment Detection via Finger Interactions with a Computer Keyboard”, Scientific Reports, 5(9678), (2015).
Download from PhysioNET