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Semi-supervised Transfer Learning for Infant Cerebellum Tissue Segmentation.

Yue Sun1, Kun Gao1, Sijie Niu1

  • 1Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, USA.

Machine Learning in Medical Imaging. MLMI (Workshop)
|February 18, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a novel semi-supervised transfer learning method for segmenting infant cerebellum tissues, improving accuracy in younger infants. The approach effectively transfers labels from older infants to younger ones, overcoming challenges in early brain development imaging.

Keywords:
Confidence mapInfant cerebellum segmentationSemi-supervised learning

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Area of Science:

  • Neuroimaging
  • Developmental Neuroscience
  • Medical Image Analysis

Background:

  • Accurate segmentation of white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) in the cerebellum is crucial for characterizing early brain development.
  • Infant cerebellum segmentation is challenging due to low tissue contrast, complex structures, and partial volume effects, making manual labeling difficult for machine learning.
  • Existing methods lack cerebellum segmentation for infants under 24 months, highlighting a gap in pediatric neuroimaging research.

Purpose of the Study:

  • To develop a robust semi-supervised transfer learning framework for segmenting cerebellum tissues in infants aged 6 to 24 months.
  • To address the challenge of limited manually labeled data in younger infants by leveraging data from older infants with higher image quality.

Main Methods:

  • A semi-supervised transfer learning framework guided by a confidence map was developed for cerebellum tissue segmentation.
  • The method utilizes reliable manual labels from 24-month-old infants (high contrast) to train models.
  • Labels are progressively transferred to younger age groups (18, 12, and 6 months) with lower tissue contrast.

Main Results:

  • The proposed method demonstrates superior performance compared to state-of-the-art techniques.
  • Performance improvements are particularly significant in segmenting the cerebellum of 6-month-old infants.
  • The confidence map guided transfer learning effectively overcomes low tissue contrast challenges in younger subjects.

Conclusions:

  • The developed semi-supervised transfer learning framework enables accurate cerebellum segmentation across a wide range of infant ages (6-24 months).
  • This approach provides a valuable tool for studying early cerebellum development, especially in populations with limited labeled neuroimaging data.
  • The method significantly advances the analysis of pediatric brain development through improved image segmentation.