Related Experiment Video
Updated: Jan 21, 2026
Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease
Published on: June 9, 2018
Improving diagnosis accuracy of brain volume abnormalities during childhood with an automated MP2RAGE-based MRI brain
Maxence Serru1, Bénédicte Marechal2, Tobias Kober2
1Pediatric Radiology department, Clocheville Hospital, CHRU of Tours, Tours, France.
Insights
Pediatric brain MRI interpretation for volume abnormalities is challenging for radiologists. Automated segmentation offers a more reliable method for diagnosing and monitoring brain volume changes in children.
Area of Science:
- Pediatric Radiology
- Neuroimaging Analysis
- Medical Image Segmentation
Background:
- Assessing pediatric brain volume abnormalities via MRI presents diagnostic challenges.
- Radiologist interpretation of brain volume can be inconsistent.
Purpose of the Study:
- To evaluate inter-radiologist reliability in pediatric brain MRI interpretation.
- To assess the efficiency of automated brain segmentation for pediatric brain volume analysis.
Main Methods:
- Prospective study of 44 pediatric patients (6 months-5 years) using 1.5T MRI with MP2RAGE sequence.
- Blind review of MRIs by one junior and two senior pediatric radiologists.
- Automated brain volumetry using MorphoBox prototype; correlation with head circumference.
Main Results:
- Radiologists demonstrated poor sensitivity in detecting brain volume abnormalities.
- High inter-observer agreement (kappa=0.93) for non-volume aspects.
- Automated quantification identified low brain volume more effectively than manual interpretation.
Conclusions:
- Radiologists' appreciation of brain volume on MRI is limited.
- Automated brain segmentation provides quantitative data for improved diagnosis and follow-up of pediatric brain volume abnormalities.
Background And Purpose:
It can be challenging to depict brain volume abnormalities in the pediatric population on magnetic resonance imaging (MRI). The aim of the study was to evaluate the inter-radiologist reliability in brain MRI interpretation, including brain volume assessment and the efficiency of an automated brain segmentation.
Materials And Methods:
We performed a single-center prospective study including 44 patients aged six months to five years recruited from the University Hospital, having a 1.5T brain MRI using a MP2RAGE sequence. All MRI were randomly and blindly reviewed by one junior and two senior pediatric radiologists. Inter-observer agreements were assessed using Fleiss' kappa coefficient. Brain volumetry (total intracranial volume (TIV), brain parenchyma, and cerebrospinal fluid volumes) was estimated using the MorphoBox prototype. Clinical head circumference (HC) and z scores were reported. A Pearson correlation coefficient was calculated between brain volumes with HC.
Results:
Twenty-four brain MRI examinations were normal and twenty were pathological. Brain volume abnormalities were poorly detected by junior and senior radiologists: sensitivities 16.67% [confidence interval 4.7-44.8], 33.33% [13-60] and 30.7% [12-58] and specificities 93.75% [79-98], 84.38% [68-93] and 77% [60-88], respectively. Brain volume apart, interobserver kappa coefficients were 0.93 between junior and seniors as well as between seniors. Brain volumes were significantly correlated with HC (P<0.0001). In patients with normal MRI, brain parenchyma volumes increased regularly with age. Low brain volume was easier to identify with automated quantification.
Conclusion:
Brain volume was poorly appreciated by radiologists. The fully automated brain segmentation used can provide quantitative data to better diagnose, describe, and follow-up brain volume abnormalities.
Related Concept Videos
02:07Improving Translational Accuracy
02:07Improving Translational Accuracy
02:23Abnormal Proliferation
03:37Uncertainty in Measurement: Accuracy and Precision
01:23Brain Waves
01:30Organization of the Brain
Hindbrain
The hindbrain, located at the base of the brain, plays a vital role in regulating automatic processes that sustain life. It includes the medulla oblongata, which is essential for...