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Using Deep Convolutional Neural Networks for Neonatal Brain Image Segmentation.

Yang Ding¹, Rolando Acosta¹, Vicente Enguix¹

¹The Canadian Neonatal Brain Platform (CNBP), Montreal, QC, Canada.

Frontiers in Neuroscience

|April 11, 2020

Summary

This summary is machine-generated.

Deep learning models like HyperDense-Net and LiviaNET can effectively segment neonatal brain tissues. HyperDense-Net showed superior performance in segmenting brain MRI scans for infants at term equivalent age.

Keywords:

T2-weighed MRI brain segmentation convolutional neural network machine learning (artificial intelligence)neonatal brain

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Area of Science:

Medical Imaging
Artificial Intelligence
Neuroscience

Background:

Deep learning excels with structured data like MRI scans.
Neonatal brain tissue segmentation is challenging due to inverted contrasts.
Prior studies showed LiviaNET and HyperDense-Net performed well on infant brain MRI.

Purpose of the Study:

To evaluate HyperDense-Net and LiviaNET for neonatal brain tissue segmentation.
To assess performance at term equivalent age using T1 and T2 MRI data.

Main Methods:

Trained and validated LiviaNET and HyperDense-Net on neonatal brain MRI data from the Developing Human Connectome Project.
Computed Dice Similarity Coefficient (DSC) to quantify segmentation accuracy for gray matter, white matter, and cerebrospinal fluid.
Compared performance of dual-modality (T1/T2) and single-modality inputs.

Main Results:

Dual-modality HyperDense-Net achieved the highest mean DSC values (0.94/0.95/0.92) for neonatal brain tissue segmentation.
Single-modality LiviaNET performed better on T2-weighted images (mean DSC 0.90/0.90/0.88) than T1-weighted.
HyperDense-Net training took 80 hours, while LiviaNET took 30 hours.

Conclusions:

Both LiviaNET and HyperDense-Net demonstrate capability in segmenting neonatal brains.
These models show potential for continuous improvement with larger neonatal brain datasets.
The models will be accessible via the Canadian Neonatal Brain Platform for research.