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High-resolution In Vivo Manual Segmentation Protocol for Human Hippocampal Subfields Using 3T Magnetic Resonance Imaging

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Dilated Dense U-Net for Infant Hippocampus Subfield Segmentation.

Hancan Zhu^1,2, Feng Shi³, Li Wang¹

¹Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.

Frontiers in Neuroinformatics

|May 10, 2019

Summary

This study introduces DUnet and ResDUnet, novel deep learning models for segmenting infant brain structures. These methods significantly improve the accuracy of infant hippocampal subfield segmentation from MRI scans.

Keywords:

deep learning dilated dense network fully convolutional network hippocampal subfield segmentation infant hippocampus

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Area of Science:

Medical Imaging
Neuroscience
Artificial Intelligence

Background:

Accurate segmentation of infant hippocampal subfields is crucial for understanding memory-related neurological disorders.
Existing segmentation methods, developed for adults, perform poorly on infant MR images due to developmental variations and low tissue contrast.

Purpose of the Study:

To develop and evaluate novel deep learning models for accurate automatic segmentation of infant hippocampal subfields.
To address the limitations of current methods in handling the unique challenges of infant brain imaging.

Main Methods:

Proposed a novel fully convolutional network (FCN) named DUnet, integrating a dilated dense network within the U-net architecture.
Further enhanced DUnet by incorporating residual connections to create Residual DUnet (ResDUnet).
These architectures leverage multi-scale features while maintaining high spatial resolution for improved feature fusion.

Main Results:

DUnet and ResDUnet demonstrated improved average Dice coefficients of 2.1% and 2.5%, respectively, compared to the standard 3D U-net for infant hippocampal subfield segmentation.
The proposed methods outperformed other state-of-the-art segmentation techniques.

Conclusions:

DUnet and ResDUnet represent significant advancements in the automatic segmentation of infant hippocampal subfields.
These models offer improved accuracy and robustness for analyzing infant brain development and related neurological conditions.