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AUTOMATED VENTRICLE PARCELLATION AND EVAN'S RATIO COMPUTATION IN PRE- AND POST-SURGICAL VENTRICULOMEGALY.

Yuli Wang1, Anqi Feng1, Yuan Xue2

  • 1Department of Biomedical Engineering, Johns Hopkins School of Medicine, USA.

Proceedings. IEEE International Symposium on Biomedical Imaging
|November 28, 2023
PubMed
Summary

We developed an AI network to automatically measure ventricle size in brain MRIs for Normal Pressure Hydrocephalus (NPH) diagnosis. This method accurately segments ventricles even with surgical implant artifacts, aiding clinical management.

Keywords:
Evan’s ratioMagnetic resonance imagingNormal pressure hydrocephalus

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Neurology

Background:

  • Normal pressure hydrocephalus (NPH) is a neurological disorder characterized by enlarged brain ventricles and significant cognitive and motor impairments.
  • Quantitative assessment of ventricular enlargement, often via the Evan's ratio (ER) on MRI, is crucial for NPH diagnosis.
  • Manual ER measurement is time-consuming and prone to inter-observer variability.

Purpose of the Study:

  • To develop an automated, robust method for segmenting ventricles in MRI scans for NPH diagnosis.
  • To enable accurate and efficient computation of the Evan's ratio (ER), even in the presence of artifacts from NPH treatments.
  • To provide a tool that assists clinicians in the diagnosis and management of NPH.

Main Methods:

  • A 3D regions-of-interest aware (ROI-aware) deep learning network was proposed for automated ventricle segmentation in MRI.
  • The method was designed to be robust to image artifacts commonly found in post-surgical NPH patients.
  • An automated approach to calculate the Evan's ratio (ER) based on the segmentation results was developed.

Main Results:

  • The proposed ROI-aware network achieved state-of-the-art performance in ventricle segmentation.
  • The method demonstrated high accuracy on both pre-operative and post-operative MRI scans, including those with shunt-related artifacts.
  • Automated ER computation based on the segmentation provided reliable quantitative measures.

Conclusions:

  • The developed automated segmentation method offers a significant advancement for NPH diagnosis and management.
  • The ROI-aware network's robustness to artifacts makes it suitable for clinical application in NPH patients undergoing or having undergone treatment.
  • This technology has the potential to streamline the diagnostic process and improve patient care for Normal Pressure Hydrocephalus.