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Automated generation of epilepsy surgery resection masks: The RAMPS pipeline

Lookup NU author(s): Callum Simpson, Dr Gerard HallORCiD, Professor Yujiang WangORCiD, Professor Peter TaylorORCiD

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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).

Abstract

© 2025 The Authors. Published under a Creative Commons Attribution 4.0 International (CC BY 4.0) license. MRI-based delineation of brain tissue removed by epilepsy surgery can be challenging due to post-operative brain shift. In consequence, most studies use manual approaches which are prohibitively time-consuming for large sample sizes, require expertise, and can be prone to errors. We propose RAMPS (Resections And Masks in Preoperative Space), an automated pipeline to generate a 3D resection mask of pre-operative tissue. Our pipeline leverages existing software including FreeSurfer, SynthStrip, Sythnseg and ANTs to generate a mask in the same space as the patient’s pre-operative T1 weighted MRI. We compare our automated masks against manually drawn masks and two other existing pipelines (Epic-CHOP and ResectVol). Comparing to manual masks (N = 87), RAMPS achieved a median (IQR) dice similarity of 0.86 (0.078) in temporal lobe resections, and 0.72 (0.32) in extratemporal resections. In comparison to other pipelines, RAMPS had higher dice similarities (N = 62) (RAMPS: 0.86, Epic-CHOP: 0.72, ResectVol: 0.72). We release a user-friendly, easy-to-use pipeline, RAMPS, open source for accurate delineation of resected tissue.

Publication metadata

Author(s): Simpson C, Hall G, Duncan JS, Wang Y, Taylor PN

Publication type: Article

Publication status: Published

Journal: Imaging Neuroscience

Year: 2025

Volume: 3

Print publication date: 10/09/2025

Online publication date: 27/08/2025

Acceptance date: 16/08/2025

Date deposited: 06/10/2025

ISSN (electronic): 2837-6056

Publisher: MIT Press

URL: https://doi.org/10.1162/IMAG.a.147

DOI: 10.1162/IMAG.a.147

Data Access Statement: Raw preoperative MRI scans are available as part of the IDEAS dataset (Taylor et al 2024). Code is available at the following location: https://github.com/cnnp-lab/RAMPS

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