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bioRxiv · 10.1101/2024.05.13.593855

Automated segmentation of epilepsy surgical resection cavities: comparison of four methods to manual segmentation

Courtney, M. R.·Sinclair, B.·Neal, A.·Nicolo, J.-P.·Kwan, P.·Law, M.·O'Brien, T. J.·Vivash, L.
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Abstract

Accurate resection cavity segmentation on MRI is important for neuroimaging research involving epilepsy surgical outcomes. Manual segmentation, the gold standard, is highly labour intensive. Automated pipelines are an efficient potential solution; however, most have been developed for use following temporal epilepsy surgery. Our aim was to compare the accuracy of four automated segmentation pipelines following surgical resection in a mixed cohort of subjects following temporal or extra temporal epilepsy surgery. We identified 4 open-source automated segmentation pipelines. Epic-CHOP and ResectVol utilise SPM-12 within MATLAB, while Resseg and Deep Resection utilise 3D U-net convolutional neural networks. We manually segmented the resection cavity of 50 consecutive subjects who underwent epilepsy surgery (30 temporal, 20 extratemporal). We calculated Dice similarity coefficient (DSC) for each algorithm compared to the manual segmentation. No algorithm identified all resection cavities. ResectVol (n=44, 88%) and Epic-CHOP (n=43, 86%) were able to detect more resection cavities than Resseg (n=22, 44%, P<0.001) and Deep Resection (n=21, 42%, P<0.001). The SPM-based pipelines (Epic-CHOP and ResectVol) performed better than the deep learning-based pipelines in the overall and extratemporal surgery cohorts, however there was no difference between methods in the temporal surgery cohort. These pipelines could be applied to machine learning studies of outcome prediction to improve efficiency in pre-processing data, however human quality control is still required.

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Courtney, M. R., Sinclair, B., Neal, A., Nicolo, J.-P., Kwan, P., Law, M., O'Brien, T. J., Vivash, L.. 2024-05-14. Automated segmentation of epilepsy surgical resection cavities: comparison of four methods to manual segmentation. https://doi.org/10.1101/2024.05.13.593855

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