ResectVol

ResectVol automates segmentation and characterization of lacunas (surgical cavities) in postoperative magnetic resonance (MR) images from patients after temporal lobe resection to generate 3D masks and estimate resection volumes for epilepsy surgical outcome analysis.

Key Features:

• Automation: Generates a 3D mask of the surgical lacuna and computes its volume from postoperative MR images.
• Validation and Performance: Performance was assessed against manually segmented data from 51 MRI scans yielding a median Dice similarity coefficient of 0.77 (interquartile range: 0.71–0.81).
• Integration with Other Modalities: Facilitates coregistration of resected areas with preoperative findings and imaging modalities including PET, SPECT, and functional MRI.
• Implementation: Implemented using MATLAB and incorporates the Statistical Parametric Mapping software (SPM12) for mask generation.

Scientific Applications:

• Epilepsy surgical outcome prediction: Provides quantitative resection volume and location data to support analyses predicting postoperative outcomes.
• Resection analysis: Enables objective, unbiased measurement of lacuna volume and spatial extent after temporal lobe resection.
• Machine learning and statistical modeling: Supplies standardized segmentation outputs suitable for use as inputs in machine learning algorithms and other predictive models.

Methodology:

Implemented in MATLAB and using SPM12 to generate 3D masks of surgical lacunas from postoperative MR images.