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Artificial intelligence for automatic cerebral ventricle segmentation and volume calculation: a clinical tool for the evaluation of pediatric hydrocephalus.

Publication ,  Conference
Quon, JL; Han, M; Kim, LH; Koran, ME; Chen, LC; Lee, EH; Wright, J; Ramaswamy, V; Lober, RM; Taylor, MD; Grant, GA; Cheshier, SH; Kestle, JRW ...
Published in: J Neurosurg Pediatr
February 1, 2021

OBJECTIVE: Imaging evaluation of the cerebral ventricles is important for clinical decision-making in pediatric hydrocephalus. Although quantitative measurements of ventricular size, over time, can facilitate objective comparison, automated tools for calculating ventricular volume are not structured for clinical use. The authors aimed to develop a fully automated deep learning (DL) model for pediatric cerebral ventricle segmentation and volume calculation for widespread clinical implementation across multiple hospitals. METHODS: The study cohort consisted of 200 children with obstructive hydrocephalus from four pediatric hospitals, along with 199 controls. Manual ventricle segmentation and volume calculation values served as "ground truth" data. An encoder-decoder convolutional neural network architecture, in which T2-weighted MR images were used as input, automatically delineated the ventricles and output volumetric measurements. On a held-out test set, segmentation accuracy was assessed using the Dice similarity coefficient (0 to 1) and volume calculation was assessed using linear regression. Model generalizability was evaluated on an external MRI data set from a fifth hospital. The DL model performance was compared against FreeSurfer research segmentation software. RESULTS: Model segmentation performed with an overall Dice score of 0.901 (0.946 in hydrocephalus, 0.856 in controls). The model generalized to external MR images from a fifth pediatric hospital with a Dice score of 0.926. The model was more accurate than FreeSurfer, with faster operating times (1.48 seconds per scan). CONCLUSIONS: The authors present a DL model for automatic ventricle segmentation and volume calculation that is more accurate and rapid than currently available methods. With near-immediate volumetric output and reliable performance across institutional scanner types, this model can be adapted to the real-time clinical evaluation of hydrocephalus and improve clinician workflow.

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Published In

J Neurosurg Pediatr

DOI

EISSN

1933-0715

Publication Date

February 1, 2021

Volume

27

Issue

2

Start / End Page

131 / 138

Location

United States

Related Subject Headings

  • Young Adult
  • Software
  • Neurology & Neurosurgery
  • Neural Networks, Computer
  • Models, Theoretical
  • Male
  • Magnetic Resonance Imaging
  • Infant, Newborn
  • Infant
  • Image Processing, Computer-Assisted
 

Citation

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Quon, J. L., Han, M., Kim, L. H., Koran, M. E., Chen, L. C., Lee, E. H., … Yeom, K. W. (2021). Artificial intelligence for automatic cerebral ventricle segmentation and volume calculation: a clinical tool for the evaluation of pediatric hydrocephalus. In J Neurosurg Pediatr (Vol. 27, pp. 131–138). United States. https://doi.org/10.3171/2020.6.PEDS20251
Quon, Jennifer L., Michelle Han, Lily H. Kim, Mary Ellen Koran, Leo C. Chen, Edward H. Lee, Jason Wright, et al. “Artificial intelligence for automatic cerebral ventricle segmentation and volume calculation: a clinical tool for the evaluation of pediatric hydrocephalus.” In J Neurosurg Pediatr, 27:131–38, 2021. https://doi.org/10.3171/2020.6.PEDS20251.
Quon, Jennifer L., et al. “Artificial intelligence for automatic cerebral ventricle segmentation and volume calculation: a clinical tool for the evaluation of pediatric hydrocephalus.J Neurosurg Pediatr, vol. 27, no. 2, 2021, pp. 131–38. Pubmed, doi:10.3171/2020.6.PEDS20251.
Quon JL, Han M, Kim LH, Koran ME, Chen LC, Lee EH, Wright J, Ramaswamy V, Lober RM, Taylor MD, Grant GA, Cheshier SH, Kestle JRW, Edwards MSB, Yeom KW. Artificial intelligence for automatic cerebral ventricle segmentation and volume calculation: a clinical tool for the evaluation of pediatric hydrocephalus. J Neurosurg Pediatr. 2021. p. 131–138.

Published In

J Neurosurg Pediatr

DOI

EISSN

1933-0715

Publication Date

February 1, 2021

Volume

27

Issue

2

Start / End Page

131 / 138

Location

United States

Related Subject Headings

  • Young Adult
  • Software
  • Neurology & Neurosurgery
  • Neural Networks, Computer
  • Models, Theoretical
  • Male
  • Magnetic Resonance Imaging
  • Infant, Newborn
  • Infant
  • Image Processing, Computer-Assisted