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Published on: February 4, 2022
Improved segmentation of cerebellar structures in children
Priya Lakshmi Narayanan1, Christopher Warton2, Natalie Rosella Boonzaier2
1Department of Human Biology, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa; MRC/UCT Medical Imaging Research Unit, Division of Biomedical Engineering, University of Cape Town, Cape Town, South Africa.
A new pediatric cerebellar atlas (CAPCA18) improves automated segmentation of brain structures in children. This atlas offers higher accuracy than existing methods, aiding pediatric neuroimaging research.
Area of Science:
- Neuroimaging
- Pediatric Neuroanatomy
- Medical Image Analysis
Background:
- Accurate localization of the cerebellar cortex in a standard coordinate system is crucial for functional studies and morphometric analyses.
- A significant gap exists in pediatric neuroimaging due to the lack of a dedicated pediatric cerebellar atlas.
Purpose of the Study:
- To develop and validate a novel probabilistic pediatric cerebellar atlas (CAPCA18) for improved automated segmentation.
- To assess the accuracy of different automated segmentation methods using the new atlas.
Main Methods:
- Construction of the Cape Town Pediatric Cerebellar Atlas (CAPCA18) using manual tracings from 18 healthy children (9-13 years).
- Implementation of multi-atlas label fusion techniques: multi-atlas majority voting (MAMV) and multi-atlas generative model (MAGM).
- Comparison of segmentation accuracy against 'gold standard' manual tracings in 14 independent test subjects.
Main Results:
- CAPCA18 demonstrated high spatial overlap (≥73%) with manual segmentations across most cerebellar lobules.
- The multi-atlas generative model (MAGM) achieved the highest segmentation accuracy (mean Dice Similarity Coefficient 0.76).
- MAGM segmentation accuracy in children was comparable to reported adult values.
Conclusions:
- The CAPCA18 atlas significantly enhances the segmentation of cerebellar structures in pediatric populations.
- This age-appropriate atlas and its associated methods provide a valuable tool for pediatric neuroimaging research.
