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The Spinal Cord

The Spinal Cord

The spinal cord is the body’s major nerve tract of the central nervous system, communicating afferent sensory information from the periphery to the brain and efferent motor information from the brain to the body. The human spinal cord extends from the hole at the base of the skull, or foramen magnum, to the level of the first or second lumbar vertebra.

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Related Experiment Video

Updated: May 16, 2025

Anatomically Inspired Three-dimensional Micro-tissue Engineered Neural Networks for Nervous System Reconstruction, Modulation, and Modeling

Anatomically Inspired Three-dimensional Micro-tissue Engineered Neural Networks for Nervous System Reconstruction, Modulation, and Modeling

Published on: May 31, 2017

DEMAC-Net: A Dual-Encoder Multiattention Collaborative Network for Cervical Nerve Pathway and Adjacent Anatomical

H Cui¹, J Duan¹, L Lin²

¹School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China.

Ultrasound in Medicine & Biology

|May 14, 2025

Summary

This summary is machine-generated.

DEMAC-Net significantly improves ultrasound image segmentation for cervical anesthesia, enhancing nerve identification and procedural safety. This AI model assists clinicians in precise needle placement, reducing risks in regional anesthesia.

Keywords:

Attention mechanism Brachial plexus Cervical plexus DEMAC-Net Deep learning Multiscale Convolution Medical Imaging Segmentation Ultrasound Image

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Three-dimensional Imaging and Analysis of Mitochondria within Human Intraepidermal Nerve Fibers

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Published on: September 29, 2017

Area of Science:

Medical Imaging
Artificial Intelligence
Anesthesiology

Background:

Cervical anesthesia techniques carry risks and require expertise.
Ultrasound imaging is valuable but struggles with segmenting small neural structures.
Accurate segmentation is crucial for safe and effective ultrasound-guided anesthesia.

Purpose of the Study:

Introduce DEMAC-Net, a dual-encoder, multi-attention network for improved segmentation of cervical and brachial plexuses.
Enhance the identification of cervical nerve pathways (CNP) and adjacent tissues.
Aid clinicians in guiding anesthesia procedures and optimizing needle insertion points.

Main Methods:

Utilized a dual-encoder architecture with Spatial Understanding Convolution Kernel (SUCK) and Spatial-Channel Attention Module (SCAM).
Incorporated Global Attention Gate (GAG) and inter-layer fusion for feature refinement and noise suppression.
Developed a new Neck Ultrasound Dataset (NUSD) with 1,500 annotated images and validated on the BUSI dataset.

Main Results:

DEMAC-Net achieved a 93.3% Dice Similarity Coefficient (DSC) on the NUSD dataset.
Demonstrated superior generalization with 87.2% DSC and 77.4% Intersection over Union (IoU) on the BUSI dataset.
Showcased consistent segmentation stability across various anatomical structures.

Conclusions:

DEMAC-Net significantly enhances segmentation accuracy for small nerves and complex structures in ultrasound images.
The network outperforms existing methods in accuracy and computational efficiency.
This framework has the potential to improve ultrasound-guided procedures like peripheral nerve blocks, leading to better clinical outcomes.