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Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

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Published on: September 25, 2019

Structure-Aware Brain Tissue Segmentation for Isointense Infant MRI Data Using Multi-Phase Multi-Scale Assistance

Jiameng Liu, Feihong Liu, Dong Nie

IEEE Journal of Biomedical and Health Informatics

|September 20, 2024

Summary

This summary is machine-generated.

This study introduces a novel framework for infant brain MRI segmentation, improving accuracy during the challenging isointense phase. The method enhances tissue differentiation for better brain development tracking and disorder diagnosis.

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Area of Science:

Neuroimaging
Medical Image Analysis
Developmental Neuroscience

Background:

Accurate brain tissue segmentation is vital for understanding brain development and diagnosing disorders.
Infant brain MRI segmentation is challenging due to overlapping gray and white matter intensities during the isointense phase (around 6 months).

Purpose of the Study:

To develop an advanced segmentation framework for accurate infant brain MRI analysis.
To overcome the limitations of the isointense phase in pediatric neuroimaging.

Main Methods:

Proposed a multi-phase, multi-scale segmentation framework combining a structure-preserved generative adversarial network (SPGAN) and a multi-phase multi-scale assisted segmentation network (MASN).
SPGAN synthesized complementary isointense and adult-like brain MRI data.
MASN utilized a two-branch network for simultaneous segmentation across phases and scales, incorporating a boundary refinement module.

Main Results:

The proposed framework demonstrated superior performance compared to seven state-of-the-art methods on the National Database for Autism Research and Baby Connectome Project datasets.
Quantitative and qualitative experiments confirmed the framework's effectiveness in segmenting infant brain MRI.

Conclusions:

The developed framework significantly improves infant brain MRI segmentation accuracy, particularly during the challenging isointense phase.
This advancement holds promise for more precise tracking of brain development and diagnosis of neurological conditions in infants.