Using Deep Convolutional Neural Networks for Neonatal Brain Image Segmentation.

ABSTRACT:

Introduction

Deep learning neural networks are especially potent at dealing with structured data, such as images and volumes. Both modified LiviaNET and HyperDense-Net performed well at a prior competition segmenting 6-month-old infant magnetic resonance images, but neonatal cerebral tissue type identification is challenging given its uniquely inverted tissue contrasts. The current study aims to evaluate the two architectures to segment neonatal brain tissue types at term equivalent age.

Methods

Both networks were retrained over 24 pairs of neonatal T1 and T2 data from the Developing Human Connectome Project public data set and validated on another eight pairs against ground truth. We then reported the best-performing model from training and its performance by computing the

SUBMITTER: Ding Y 

PROVIDER: S-EPMC7114297 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature