Automated generation of epilepsy surgery resection masks; The RAMPS pipeline
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866915170174894080 |
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| author | Simpson, Callum Hall, Gerard Duncan, John S. Wang, Yujiang Taylor, Peter N. |
| author_facet | Simpson, Callum Hall, Gerard Duncan, John S. Wang, Yujiang Taylor, Peter N. |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2502_17287 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Automated generation of epilepsy surgery resection masks; The RAMPS pipeline Simpson, Callum Hall, Gerard Duncan, John S. Wang, Yujiang Taylor, Peter N. Neurons and Cognition 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. |
| title | Automated generation of epilepsy surgery resection masks; The RAMPS pipeline |
| topic | Neurons and Cognition |
| url | https://arxiv.org/abs/2502.17287 |