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Automated Segmentation and Volumetry for Meningioma Using Deep Learning

NCT ID: NCT05093751

Last Updated: 2021-10-26

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

COMPLETED

Total Enrollment

600 participants

Study Classification

OBSERVATIONAL

Study Start Date

2013-03-23

Study Completion Date

2021-09-30

Brief Summary

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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.

Detailed Description

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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.

Conditions

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Meningioma Artificial Intelligence

Keywords

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Meningioma Artificial intelligence autosegmentation volumetry

Study Design

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Observational Model Type

CASE_ONLY

Study Time Perspective

RETROSPECTIVE

Study Groups

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Meningioma patients

Observation

Intervention Type OTHER

This study does not involve any intervention to subjects.

Interventions

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Observation

This study does not involve any intervention to subjects.

Intervention Type OTHER

Eligibility Criteria

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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
Minimum Eligible Age

18 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

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Seoul National University Hospital

OTHER

Sponsor Role lead

Responsible Party

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Chul-Kee Park

Professor

Responsibility Role PRINCIPAL_INVESTIGATOR

Other Identifiers

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SNUH-MNG-AI001

Identifier Type: -

Identifier Source: org_study_id