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



Hajikarimloo B1 ; Mohammadzadeh I2 ; Habibi MA3 ; Kooshki A4 ; Aghajani S5 ; Tajvidi M6 ; Hashemi R1 ; Hooshmand M1 ; Bana S1 ; Najari D1 ; Tavanaei R1 ; Akhlaghpasand M1
Authors
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Authors Affiliations
  1. 1. Department of Neurological Surgery, Shohada Tajrish Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran
  2. 2. Skull Base Research Center, Loghman-Hakim Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran
  3. 3. Department of Neurosurgery, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
  4. 4. Student Research Committee, Birjand University of Medical Sciences, Birjand, Iran
  5. 5. Department of Medicine, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
  6. 6. Student Research Committee, Abadan University of Medical Sciences, Abadan, Iran

Source: World Neurosurgery Published:2025


Abstract

Background: Ventricular segmentation is a critical step in neuroimaging data evaluation, particularly in hydrocephalus. Current methods are mainly based on 2-dimensional measurements and ratios. Traditional manual and semiautomatic ventricular segmentation are time-consuming, operator-based, and lack flexibility in handling numerous radiological features. Recently, deep learning (DL) models have been developed to perform ventricular segmentation and have shown promising outcomes. The objective of the current study was to evaluate the performance of DL-based models in ventricular segmentation in the hydrocephalus setting. Methods: On December 5, 2024, a systematic search was conducted using an individualized search query in 4 electronic databases: PubMed, Embase, Scopus, and Web of Science. Studies that reported the mean dice similarity coefficient (DSC) of DL-based models in ventricular segmentation in patients with hydrocephalus were included. The mean DSC for the best-performance model was extracted. Results: Twenty-four studies with 2911 patients were included. The mean DSC ranged from 0.671 to 0.99 across the best-performance models. The meta-analysis revealed a pooled mean DSC of 0.89 (95% CI: 0.84–92). The subgroup analysis yielded a pooled mean DSC of 0.88 (95% CI: 0.80–0.96) for magnetic resonance imaging-based models, 0.91 (95% CI: 0.86–0.95) for computed tomography-based models, and 0.84 (95% CI: 0.81–0.87) for ultrasound-based best-performance DL-based models. Conclusions: DL-based models have demonstrated favorable outcomes in ventricular segmentation in patients with hydrocephalus. Application of these models in clinical practice can optimize the treatment protocol and enhance the clinical outcomes of hydrocephalus patients. © 2025 The Author(s)
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