Abstract #1539

Auto-Segmentation for Diffuse Pachymeningeal Enhancement in Patients with Spontaneous Intracranial Hypotension

Po-Hsun Su¹, Yen-Feng Wang^2,3,4, Pei-Yun Wu¹, Shuu-Jiun Wang^2,3,4, and Chia-Feng Lu¹

¹Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei, Taiwan, ²School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan, ³Department of Neurology, Neurological Institute, Taipei Veterans General Hospital, Taipei, Taiwan, ⁴Brain Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan

Synopsis

Keywords: Diagnosis/Prediction, Brain, Spontaneous Intracranial Hypotension, Deep Learning, Automated Segmentation

Motivation: Diffuse pachymeningeal enhancement (DPE), an imaging characteristic of spontaneous intracranial hypotension (SIH) on post-gadolinium brain magnetic resonance imaging, is not always present. Therefore, an objective approach to detect DPE is essential.

Goal(s): We aimed to investigate an auto-segmentation model to delineate DPE in SIH patients.

Approach: 104 SIH patients with gadolinium-enhanced T1-weighted MRIs were enrolled. A U-Net architecture was employed for the auto-segmentation using the manually contoured DPE as ground truth.

Results: The auto-segmentation model achieved satisfactory performance with a median dice score of 0.88, indicating the ability to delineate DPE in SIH patients.

Impact: This study developed an auto-segmentation model for diffuse pachymeningeal enhancement on brain MRI in SIH patients. It demonstrates the potential to integrate an automatic detection process to assist in the clinical diagnosis and management of SIH patients.

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