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Functional Brain Systems: Limbic System

Functional Brain Systems: Limbic System

The limbic system, often called the "emotional brain," is a complex set of structures located deep within the brain. The intricate network of the limbic system supports a wide range of psychological functions, from emotional regulation to memory formation and sensory processing. This functional brain region encompasses specific parts of the diencephalon and the cerebrum, integrating the higher mental functions of the cerebral cortex with the primitive emotional responses of the deep brain...

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Updated: Sep 21, 2025

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

ID-Seg: an infant deep learning-based segmentation framework to improve limbic structure estimates.

Yun Wang^1,2, Fateme Sadat Haghpanah³, Xuzhe Zhang⁴

¹Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA.

Brain Informatics

|May 28, 2022

Summary

This summary is machine-generated.

We developed Infant Deep learning SEGmentation Framework (ID-Seg) for accurate infant brain MRI segmentation. ID-Seg improves accuracy over existing methods and shows stronger associations with infant behavioral problems.

Keywords:

Amygdala Behavioral problems Convolutional neural networks Deep learning Hippocampus Infant neuroimaging Segmentation

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Last Updated: Sep 21, 2025

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Area of Science:

Neuroimaging
Developmental Neuroscience
Artificial Intelligence in Medicine

Background:

Infant brain magnetic resonance imaging (MRI) is crucial for studying early neurodevelopment.
Segmenting small limbic structures in infant MRI is challenging due to low contrast and high curvature.
Current deep learning models for infant MRI segmentation often use small datasets, risking generalization issues.

Purpose of the Study:

To develop and validate a robust deep learning framework for segmenting limbic structures in infant brain MRI.
To compare the performance of the new framework against established methods like the Developmental Human Connectome (dHCP) pipeline.
To assess the clinical relevance of segmentations by linking them to infant behavioral outcomes.

Main Methods:

Leveraged a large infant MRI dataset (n=473) and transfer learning to pre-train a deep convolutional neural network.
Developed the Infant Deep learning SEGmentation Framework (ID-Seg) using a leave-one-out cross-validation strategy for fine-tuning.
Evaluated ID-Seg on two independent datasets and compared its performance with the dHCP pipeline using Dice Similarity Coefficient (DSC), Intra-class Correlation (ICC), and Average Surface Distance (ASD).

Main Results:

ID-Seg achieved high segmentation accuracy with a mean DSC of 0.87, ICC of 0.93, and ASD of 0.31 mm across datasets.
ID-Seg significantly improved segmentation accuracy compared to the dHCP pipeline.
Estimates from ID-Seg showed stronger associations with behavioral problems in infants at age 2 compared to dHCP estimates.

Conclusions:

ID-Seg offers a robust and accurate method for segmenting amygdala and hippocampus in infant brain MRI.
The framework demonstrates superior performance and clinical relevance compared to existing pipelines.
Future work should focus on multi-site validation and extending ID-Seg to other brain regions.