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An attention-based context-informed deep framework for infant brain subcortical segmentation.

Liangjun Chen1, Zhengwang Wu1, Fenqiang Zhao1

  • 1Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.

Neuroimage
|February 6, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a novel deep learning framework for precise infant brain subcortical segmentation using magnetic resonance imaging (MRI). The method enhances accuracy in segmenting these crucial brain structures, aiding in developmental studies and disorder diagnosis.

Keywords:
BrainInfantMRISubcortical segmentation

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Area of Science:

  • Neuroimaging
  • Medical Image Analysis
  • Developmental Neuroscience

Background:

  • Accurate segmentation of infant subcortical structures in MRI is vital for understanding early brain development and diagnosing disorders.
  • Infant brain MRI presents challenges due to dynamic appearance changes, low contrast, and small structure sizes.

Purpose of the Study:

  • To develop a precise segmentation method for infant subcortical structures using a novel deep learning framework.
  • To improve the accuracy and generalizability of automated segmentation in challenging infant neuroimaging data.

Main Methods:

  • A context-guided, attention-based, coarse-to-fine deep framework utilizing multi-modal MRI (T1w, T2w, T1w/T2w ratio).
  • Employs an SDM-Unet for coarse segmentation and a multi-source, multi-path attention Unet (M2A-Unet) for refined segmentation.
  • Incorporates 3D spatial and channel attention, along with inner/outer boundary labels for enhanced precision.

Main Results:

  • The proposed framework achieved higher segmentation accuracy compared to eleven state-of-the-art methods on infant MR images.
  • Demonstrated robust performance and good generalizability on an independent neonatal MR image dataset.
  • Qualitative and quantitative evaluations confirmed the superior accuracy of the proposed method.

Conclusions:

  • The developed context-guided, attention-based framework offers a significant advancement in precise infant subcortical segmentation.
  • This method holds promise for improving the diagnosis and study of early brain development and related disorders.
  • The framework's generalizability suggests its potential utility across different infant and neonatal populations.