Automated generation of epilepsy surgery resection masks; The RAMPS pipeline

TL;DR

The paper introduces RAMPS, an automated, open-source pipeline that accurately generates 3D resection masks from pre-operative MRI, improving efficiency and consistency over manual methods and existing pipelines in epilepsy surgery analysis.

Contribution

RAMPS is a novel automated pipeline that leverages existing software to produce accurate resection masks in pre-operative space, outperforming other methods in similarity to manual masks.

Findings

RAMPS achieved a median dice similarity of 0.86 in temporal resections.

RAMPS outperformed Epic-CHOP and ResectVol in dice similarity.

The pipeline is user-friendly and open source.

Abstract

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.