Automatic lateral ventricle and choroid plexus segmentation method in infant brain MR images

Area of Science:

• Neuroimaging
• Medical Image Analysis
• Deep Learning

Background:

• Accurate segmentation of lateral ventricles (LV) and choroid plexus (CP) in infant brain MRI is crucial for understanding cerebrospinal fluid dynamics and early neurodevelopment.
• Existing segmentation methods for adult populations often perform poorly on infant data due to anatomical differences, low contrast, and motion artifacts.
• These methods frequently result in misclassification of tissue boundaries in pediatric neuroimaging.

Purpose of the Study:

• To develop a fully automated deep learning method for joint segmentation of LV and CP in infant brain MRI.
• To address the challenges of rapid anatomical changes, low tissue contrast, and motion artifacts in infant neuroimaging.
• To validate the clinical adaptability and performance of the proposed segmentation method on independent datasets.

Main Methods:

• A fully automated deep learning approach for joint LV and CP segmentation using T1-weighted MRI was developed.
• An anatomy-aware loss function was integrated to enforce the topological constraint of CP containment within the LV.
• The method was validated on two independent datasets (Baby Connectome Project and an in-house dataset) using Dice score, 95% Hausdorff distance (HD95), and average symmetric surface distance (ASSD).

Main Results:

• The method achieved high Dice scores: 0.818 ± 0.075 for LV and 0.827 ± 0.084 for CP on the BCP dataset.
• On the in-house dataset, Dice scores were even higher: 0.964 ± 0.060 for LV and 0.932 ± 0.059 for CP.
• Quantitative validation using HD95 and ASSD demonstrated the method's accuracy and consistency across both datasets.

Conclusions:

• The proposed deep learning method effectively segments LV and CP in infant brain MRI, overcoming limitations of previous approaches.
• The method ensures anatomical consistency without requiring manual annotation, making it suitable for large-scale studies.
• This technique has the potential to significantly advance research into CP morphology and its role in early neurodevelopment.