Abstract #1541

Automated Segmentation of Posterior Cranial Fossa in Spontaneous Intracranial Hypotension

Pei-Yun Wu¹, Yen-Feng Wang^2,3,4, Po-Hsun Su¹, 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, Brain Sagging, Deep Learning, Automated Segmentation

Motivation: Spontaneous intracranial hypotension (SIH) is caused by spinal cerebrospinal fluid leakage. Brain sagging sign (BSS) is one of the commonly observed MRI characteristics of SIH; however, reliable quantitative measurement is lacking.

Goal(s): This study aimed to develop an auto-segmentation model to identify tissues within the posterior cranial fossa (PCF) and quantify the severity of BSS accordingly.

Approach: We used 2D U-Net to automatically segment the brainstem, cerebellum, fourth ventricle, and PCF with CSF space on the mid-sagittal plane of the T1-weighted image.

Results: The segmentation model achieved an overall accuracy of 93%, indicating its potential to quantify BSS in SIH.

Impact: This study built the foundation for the automatic detection of SIH from structural MRI, paving the way for future research to facilitate the diagnosis by quantifying brain sagging signs and enhancing the efficiency and accuracy of clinical management.

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