Accuracy and practical aspects of semi- and fully automatic segmentation methods for resected brain areas.

Neuroradiology
Karin GauNiels Alexander Foit

Abstract

Precise segmentation of brain lesions is essential for neurological research. Specifically, resection volume estimates can aid in the assessment of residual postoperative tissue, e.g. following surgery for glioma. Furthermore, behavioral lesion-symptom mapping in epilepsy relies on accurate delineation of surgical lesions. We sought to determine whether semi- and fully automatic segmentation methods can be applied to resected brain areas and which approach provides the most accurate and cost-efficient results. We compared a semi-automatic (ITK-SNAP) with a fully automatic (lesion_GNB) method for segmentation of resected brain areas in terms of accuracy with manual segmentation serving as reference. Additionally, we evaluated processing times of all three methods. We used T1w, MRI-data of epilepsy patients (n = 27; 11 m; mean age 39 years, range 16-69) who underwent temporal lobe resections (17 left). The semi-automatic approach yielded superior accuracy (p < 0.001) with a median Dice similarity coefficient (mDSC) of 0.78 and a median average Hausdorff distance (maHD) of 0.44 compared with the fully automatic approach (mDSC 0.58, maHD 1.32). There was no significant difference between the median percent volume difference of the ...Continue Reading

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Citations

Oct 6, 2021·Epilepsia Open·Raphael F CassebFernando Cendes

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Methods Mentioned

BETA
surgical resection
surgical resections

Software Mentioned

SPM
MATLAB
_ GNB
ITK
lesion
SPSS
GNB
Windows
snap

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