The Imaging Database for Epilepsy And Surgery (IDEAS)
Peter N. Taylor, Yujiang Wang, Callum Simpson, Vytene Janiukstyte,, Jonathan Horsley, Karoline Leiberg, Beth Little, Harry Clifford, Sophie, Adler, Sjoerd B. Vos, Gavin P Winston, Andrew W McEvoy, Anna Miserocchi, Jane, de Tisi, John S Duncan

TL;DR
The IDEAS dataset provides a comprehensive, open-source collection of MRI scans and clinical data from 442 epilepsy patients, facilitating machine learning research for lesion detection and surgical outcome prediction.
Contribution
This paper introduces a large, well-annotated MRI dataset for epilepsy, including resection masks and detailed clinical information, supporting advancements in AI-based diagnosis and treatment.
Findings
Replicated long-term seizure freedom outcomes (~50%)
Confirmed group-level atrophy in patients versus controls
Mapped resection locations predominantly in temporal and frontal lobes
Abstract
Magnetic resonance imaging (MRI) is a crucial tool to identify brain abnormalities in a wide range of neurological disorders. In focal epilepsy MRI is used to identify structural cerebral abnormalities. For covert lesions, machine learning and artificial intelligence algorithms may improve lesion detection if abnormalities are not evident on visual inspection. The success of this approach depends on the volume and quality of training data. Herein, we release an open-source dataset of preprocessed MRI scans from 442 individuals with drug-refractory focal epilepsy who had neurosurgical resections, and detailed demographic information. The MRI scan data includes the preoperative 3D T1 and where available 3D FLAIR, as well as a manually inspected complete surface reconstruction and volumetric parcellations. Demographic information includes age, sex, age of onset of epilepsy, location of…
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Taxonomy
TopicsDiet and metabolism studies
