University College London

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EPISURG: a dataset of postoperative magnetic resonance images (MRI) for quantitative analysis of resection neurosurgery for refractory epilepsy

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posted on 2020-12-16, 17:03 authored by Fernando Pérez-García, Roman RodionovRoman Rodionov, Ali Alim-Marvasti, Rachel Sparks, John DuncanJohn Duncan, Sebastien Ourselin
EPISURG is a clinical dataset of T1-weighted magnetic resonance images (MRI) from 430 epileptic patients who underwent resective brain surgery at the National Hospital of Neurology and Neurosurgery (Queen Square, London, United Kingdom) between 1990 and 2018.

The NIfTI files are anonymised and the images have been defaced to further protect the patients' identity.

The dataset comprises 430 postoperative MRI. The corresponding preoperative MRI is present for 269 subjects.

Three human raters segmented the resection cavity on partially overlapping subsets of EPISURG:

- Rater 1: 133 subjects (researcher in neuroimaging)
- Rater 2: 34 subjects (clinical scientist)
- Rater 3: 33 subjects (neurologist)


If you use this dataset for your research please cite the following publications:

Pérez-García F., Rodionov R., Alim-Marvasti A., Sparks R., Duncan J.S., Ourselin S. (2020) Simulation of Brain Resection for Cavity Segmentation Using Self-supervised and Semi-supervised Learning. In: Martel A.L. et al. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. Lecture Notes in Computer Science, vol 12263. Springer, Cham.

Pérez-García F., Rodionov R., Alim-Marvasti A., Sparks R., Duncan J.S., Ourselin S. EPISURG: MRI dataset for quantitative analysis of resective neurosurgery for refractory epilepsy. University College London (2020). DOI 10.5522/04/9996158.v1

Graphical user interface (GUI)

The 3D Slicer extension EPISURG may be used to visualise the dataset:

Data use agreement

The EPISURG data are distributed to the greater scientific community under the following terms:

1. You will not attempt to establish the identity or to make contact with any of the included subjects.
2. You will acknowledge the use of EPISURG data and data derived from EPISURG data when publicly presenting any results or algorithms that benefitted from their use. Papers, book chapters, books, posters, oral presentations, and all other printed and digital presentations of results derived from EPISURG data should cite the publications listed above.
3. You will not further disclose these data beyond the uses outlined in this agreement and understand that redistribution of data in any manner is prohibited.
4. You will require anyone on your team who uses these data, or anyone with whom you share these data to comply with this data use agreement.


Wellcome Innovator Award (218380/Z/19/Z/)