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Deep Learning-Based Models for Ventricular Segmentation in Hydrocephalus: A Systematic Review and Meta-Analysis.

Bardia Hajikarimloo¹, Ibrahim Mohammadzadeh², Mohammad Amin Habibi³

¹Department of Neurological Surgery, Shohada Tajrish Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran.

World Neurosurgery

|April 30, 2025

Summary

Deep learning models show strong performance in segmenting ventricles for hydrocephalus patients. These advanced methods can improve treatment and patient outcomes in clinical settings.

Keywords:

Computed tomography Deep learning Hydrocephalus Magnetic resonance imaging Normal pressure hydrocephalus

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Area of Science:

Neuroimaging
Medical Imaging Analysis
Artificial Intelligence in Medicine

Background:

Ventricular segmentation is crucial for hydrocephalus evaluation.
Traditional segmentation methods are time-consuming and lack flexibility.
Deep learning (DL) models offer promising advancements in segmentation accuracy.

Purpose of the Study:

To evaluate the performance of DL-based models for ventricular segmentation in hydrocephalus.
To synthesize existing research on DL model efficacy in this specific clinical context.

Main Methods:

A systematic literature search was performed across PubMed, Embase, Scopus, and Web of Science.
Studies reporting the mean Dice Similarity Coefficient (DSC) for DL models in hydrocephalus ventricular segmentation were included.
The mean DSC of the best-performing DL model in each study was extracted for meta-analysis.

Main Results:

Twenty-four studies involving 2911 patients were analyzed.
The pooled mean DSC for DL-based ventricular segmentation in hydrocephalus was 0.89.
Subgroup analysis showed high performance across MRI (0.88), CT (0.91), and ultrasound (0.84) based DL models.

Conclusions:

DL-based models demonstrate significant efficacy for ventricular segmentation in hydrocephalus.
Implementing these models can potentially optimize hydrocephalus treatment protocols.
Improved segmentation accuracy may lead to enhanced clinical outcomes for hydrocephalus patients.