Automated Segmentation and Volumetry for Meningioma Using Deep Learning

Seoul National University

Status: Completed

Conditions: Meningioma; Artificial Intelligence

Treatments: Other: Observation

Study type: Observational

Funder types: Other

Identifiers

NCT05093751

SNUH-MNG-AI001

Details and patient eligibility

About

U-Net-based architectures will be applied to 500 contrast-enhanced axial MR images of different patients from a single institution after manual segmentation of meningioma, of which 50 were used for testing. Tumor volumetry after autosegmentation by trained U-Net-based architecture is final goal.

Full description

U-Net-based architectures will be applied to 500 contrast-enhanced axial MR images of different patients from a single institution after manual segmentation of meningioma, of which 50 were used for testing. After preprocessing with Z-isotropification and intensity normalization of images, 3 U-Net-based networks (2D U-Net, Attention U-Net, 3D U-Net) and 3 nnU-Net-based networks (2D nnU-Net, Attention nnU-Net, 3D nnU-Net) will be trained with meningioma-segmented images. For applying to 3D networks, sagittal and coronal images will be reconstructed using axial images. After prediction, the cut-off of the probability function, which is a trade-off, will be obtained with the Gaussian Mixture Modeling algorithm using the probability density function. The voxels having a probability function higher than that will be finally predicted as meningioma. Tumor volume is calculated as the sum of the product of segmented area and thickness of axial images. For performance evaluation, dice similarity coefficient (DSC), precision, and recall will be evaluated compared with manually segmented voxels for validation datasets. The results of volumetry of each model will be compared with manual segmentation-based volume through Pearson's correlation analysis.

Enrollment: 600 patients

Sex: All

Ages: 18+ years old

Volunteers: No Healthy Volunteers

Inclusion criteria

• Radiologically diagnosed meningioma by MRI

Exclusion criteria

• under 18 years old
• Multiple meningiomas
• Orbital meningioma
• Any prior treatment for intracranial meningioma before registration

Trial design

600 participants in 1 patient group

Meningioma patients

Treatment:

Other: Observation