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.
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