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Automatic brain labeling via multi-atlas guided fully convolutional networks.

Longwei Fang1, Lichi Zhang2, Dong Nie3

  • 1Research Center for Brain-inspired Intelligence and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences(CAS), Beijing, 100190, China; University of Chinese Academy of Sciences, Beijing, China; Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC, USA.

Medical Image Analysis
|November 18, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces a novel multi-atlas guided fully convolutional network (MA-FCN) for efficient and accurate MR brain image labeling. The MA-FCN method improves upon traditional approaches by leveraging prior knowledge from training atlases, enhancing automated neuroimaging analysis.

Keywords:
Brain image labelingFully convolutional networkMulti-atlas-based methodPatch-based labeling

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Area of Science:

  • Neuroimaging
  • Medical Image Analysis
  • Computer Vision

Background:

  • Manual MR brain image labeling is time-consuming and labor-intensive.
  • Traditional multi-atlas methods rely on accurate but slow non-rigid registration.
  • Patch-based methods offer alternatives but often require handcrafted features.

Purpose of the Study:

  • To develop an automated MR brain image labeling method that improves accuracy and efficiency.
  • To integrate prior knowledge from multiple atlases into a deep learning framework.
  • To overcome limitations of traditional registration-dependent and feature-engineered methods.

Main Methods:

  • Proposed a multi-atlas guided fully convolutional network (MA-FCN).
  • Trained the MA-FCN model in a patch-based manner using training image patches and neighboring, affine-aligned atlas patches.
  • Utilized guidance information from atlas patches to enhance the discriminative ability of the network.

Main Results:

  • The proposed MA-FCN method significantly outperformed conventional fully convolutional networks (FCNs).
  • Demonstrated superior performance compared to several state-of-the-art MR brain labeling methods.
  • Experimental results on diverse datasets validated the effectiveness of the MA-FCN approach.

Conclusions:

  • The MA-FCN method offers a powerful and effective solution for automated MR brain image labeling.
  • Integrating multi-atlas prior knowledge into deep learning significantly enhances labeling performance.
  • This approach holds promise for advancing neuroimaging analysis by reducing manual effort and improving accuracy.