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. 2021 Dec;6(4):720-726.
doi: 10.1002/epi4.12546. Epub 2021 Oct 12.

ResectVol: A tool to automatically segment and characterize lacunas in brain images

Raphael F Casseb  1 , Brunno M de Campos  1 , Marcia Morita-Sherman  2 , Amr Morsi  2 , Efstathios Kondylis  2 , William E Bingaman  2 , Stephen E Jones  2 , Lara Jehi  2 , Fernando Cendes  1
Affiliations

ResectVol: A tool to automatically segment and characterize lacunas in brain images

Raphael F Casseb et al. Epilepsia Open. 2021 Dec.

Abstract

Objective: To assess and validate the performance of a new tool developed for segmenting and characterizing lacunas in postoperative MR images of epilepsy patients.

Methods: A MATLAB-based pipeline was implemented using SPM12 to produce the 3D mask of the surgical lacuna and estimate its volume. To validate its performance, we compared the manual and automatic lacuna segmentations obtained from 51 MRI scans of epilepsy patients who underwent temporal lobe resections.

Results: The code is consolidated as a tool named ResectVol, which can be run via a graphical user interface or command line. The automatic and manual segmentation comparison resulted in a median Dice similarity coefficient of 0.77 (interquartile range: 0.71-0.81).

Significance: Epilepsy surgery is the treatment of choice for pharmacoresistant focal epilepsies, and despite the extensive literature on the subject, we still cannot predict surgical outcomes accurately. As the volume and location of the resected tissue are fundamentally relevant to this prediction, researchers commonly perform a manual segmentation of the lacuna, which presents human bias and does not provide detailed information about the structures removed. In this study, we introduce ResectVol, a user-friendly, fully automatic tool to accomplish these tasks. This capability enables more advanced analytical techniques applied to surgical outcomes prediction, such as machine-learning algorithms, by facilitating coregistration of the resected area and preoperative findings with other imaging modalities such as PET, SPECT, and functional MRI ResectVol is freely available at https://www.lniunicamp.com/resectvol.

Keywords: Epilepsy; MRI; automatic segmentation; surgical outcome; volumetry.

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Figures

FIGURE 1
FIGURE 1
Performance metrics. Box plots for the (A) relative difference between approaches and (B) for the Dice coefficient, and (C) the scatter plot of the volumetric measurements with the linear fit (solid line; coefficient of determination (R²) = 0.638) and the reference line (dashed)
FIGURE 2
FIGURE 2
Segmentation examples. Manual (red) and automatic (cyan) lacuna masks overlayed onto the postoperative images. The images represent the (A) best (Dice similarity coefficient (DSC) = 0.89), (B) median (DSC = 0.76), and (C) worst (DSC = 0.23) DSCs obtained by the comparison of the manual and the automatic segmentation approaches
FIGURE 3
FIGURE 3
Characterization examples. (A) Brain structure masks overlayed onto the postoperative image and (B) the corresponding description file
FIGURE 4
FIGURE 4
User interface. (A) Graphical user interface and (B) the selection of the parent folder containing all subjects' directories
See this image and copyright information in PMC

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