3D deep neural network to automatically identify TSC structural brain pathology based on MRI

Mahdieh Shabanian; Abdullah Al Zubaer Imran; Adeel Siddiqui; Robert L. Davis M.D.; John J. Bissler M.D.

doi:10.1117/12.2611280

4 April 2022 3D deep neural network to automatically identify TSC structural brain pathology based on MRI

Mahdieh Shabanian, Abdullah Al Zubaer Imran, Adeel Siddiqui, Robert L. Davis M.D., John J. Bissler M.D.

Proceedings Volume 12032, Medical Imaging 2022: Image Processing; 120322B (2022) https://doi.org/10.1117/12.2611280
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Conference Poster

Abstract

During childhood, neurological involvement in tuberous sclerosis complex (TSC) is a leading cause of death. Neurological involvement, including epilepsy, can cause significant long-term sequelae in children. Brain involvement in TSC can be detected by magnetic resonance imaging (MRI). Still, neuroimaging analysis is time-and labor-intensive, begging the need for automated approaches to these tasks to improve speed, accuracy, and availability. We explored the general feasibility of using three-dimensional convolutional neural networks (CNNs) to automatically enhance image diagnosis quality and consistency to identify anatomical abnormalities in TSC children. We trained the 3D CNN on axial T1-weighted, axial T2-weighted FLAIR, and 3DT1-FSPGR weighted images from 296 TSC and 245 Normal cases from birth to 8 years of age acquired at LeBonheur Children’s Hospital. In the best performing approach, we achieved an accuracy of 0.86 [95% CI:0.76-0.97] with 0.95% AUC. The code can be found in https://github.com/shabanian2018/TSC3DCNN

Citation Download Citation

Mahdieh Shabanian, Abdullah Al Zubaer Imran, Adeel Siddiqui, Robert L. Davis M.D., and John J. Bissler M.D. "3D deep neural network to automatically identify TSC structural brain pathology based on MRI", Proc. SPIE 12032, Medical Imaging 2022: Image Processing, 120322B (4 April 2022); https://doi.org/10.1117/12.2611280

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