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Anatomy of the Adrenal Glands

Anatomy of the Adrenal Glands

The adrenal or supra-renal glands, situated above the kidneys and aligned with the twelfth rib, are paired pyramid-shaped structures crucial for the body's stress response. During stress, these glands secrete hormones vital for adaptive physiological reactions.
These glands possess a distinctive yellow tinge due to the stored cholesterol and fatty acids required for hormone synthesis. They are encased in a fibrous capsule and cushioned by fat.
The adrenal gland comprises two distinct...

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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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Adrenal Volume Quantitative Visualization Tool by Multiple Parameters and an nnU-Net Deep Learning Automatic

Yi Li¹, Yingnan Zhao², Ping Yang¹

¹Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China.

Journal of Imaging Informatics in Medicine

|July 2, 2024

Summary

A new deep learning tool automates adrenal gland volume measurement, improving accuracy for diseases like adrenal hyperplasia. This advanced model enhances clinical screening and monitoring using low-dose CT scans.

Keywords:

Adrenal gland Convolutional neural network Image segmentation Volume quantitative nnU-Net

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Area of Science:

Radiology
Medical Imaging
Artificial Intelligence

Background:

Adrenal gland size abnormalities are linked to various diseases.
Accurate adrenal gland volume monitoring is crucial for diagnosing conditions like adrenal hyperplasia and adenocarcinoma.
Existing segmentation models struggle with diverse imaging parameters and low-dose scans, limiting clinical use.

Purpose of the Study:

To develop a fully automated tool for adrenal gland volume quantification and visualization.
To address limitations in current deep learning models for adrenal gland segmentation, especially concerning low-dose imaging.
To enhance the accuracy and adaptability of adrenal gland segmentation for clinical applications.

Main Methods:

Development of a fully automated adrenal gland segmentation tool utilizing the no new U-Net (nnU-Net) architecture.
Training the model on a large, diverse dataset encompassing multiple imaging parameters, machine types, radiation doses, and adrenal gland morphologies.
Validation of the tool's performance on general and low-dose CT scans.

Main Results:

The developed tool achieved a high overall Dice coefficient of 0.88 for adrenal gland segmentation.
The model demonstrated strong performance on low-dose CT scans, with a Dice coefficient of 0.87.
The tool exhibited superior accuracy and broader adaptability compared to other deep learning models and existing nnU-Net tools.

Conclusions:

The automated tool provides accurate and adaptable adrenal gland volume quantification, meeting clinical needs for screening, monitoring, and preoperative visualization.
The nnU-Net based approach overcomes limitations of previous models, particularly in handling low-dose imaging parameters.
This technology has the potential to significantly improve the clinical management of adrenal gland diseases.