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Related Concept Videos

Adrenal Gland Disorders01:27

Adrenal Gland Disorders

Adrenal gland disorders manifest when the production of adrenal hormones deviates from the norm, resulting in either excessive or insufficient concentrations.
Adrenal insufficiency, characterized by insufficient cortisol and aldosterone production, leads to conditions like Addison's disease. This disorder, affecting the adrenal cortex, exhibits symptoms such as skin bronzing, dehydration, low blood pressure, fatigue, and weight loss. Congenital adrenal hyperplasia, a genetic ailment causing...
Anatomy of the Adrenal Glands01:17

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 regions...
Hormones of the Adrenal Glands01:31

Hormones of the Adrenal Glands

Adrenal hormones play a pivotal role in maintaining the body's electrolyte balance and orchestrating responses to stress, showcasing the intricate functions of the adrenal cortex and medulla.
The adrenal cortex, a powerhouse of hormone synthesis, generates over two dozen corticosteroid hormones. The zona glomerulosa produces mineralocorticoids, exemplified by aldosterone, influencing the electrolyte composition of body fluids. The synthesis of glucocorticoids such as cortisol and corticosterone...
Survival Tree01:19

Survival Tree

Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a survival tree begins...
Adrenergic Receptors (Adrenoceptors): Classification01:27

Adrenergic Receptors (Adrenoceptors): Classification

Adrenergic receptors, or adrenoceptors, respond to the autonomic neurotransmitter noradrenaline and other endogenous catecholamine agonists. They are classified into two main families, α and β, based on their pharmacological response and are further subdivided depending on their location, elicited response, and affinity to specific agonists or antagonists.
α-Adrenoceptors
α-Adrenoceptors are classified into two main subtypes: α1 and α2. The α1 adrenoceptors, which are found on postsynaptic...
Classification of Illness01:17

Classification of Illness

The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe and...

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Related Experiment Video

Updated: May 14, 2026

A Novel Method: Super-selective Adrenal Venous Sampling
06:08

A Novel Method: Super-selective Adrenal Venous Sampling

Published on: September 15, 2017

Adrenal gland abnormality detection using random forest classification.

Ganesh Saiprasad1, Chein-I Chang, Nabile Safdar

  • 1Department of Electrical Engineering, University of Maryland Baltimore County, 1000 Hilltop Circle, Baltimore, MD, 21250, USA, ganeshs1@umbc.edu.

Journal of Digital Imaging
|January 25, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces an automated method for detecting adrenal abnormalities using computed tomography (CT) scans. The novel approach accurately identifies adrenal gland issues, improving diagnostic capabilities.

Related Experiment Videos

Last Updated: May 14, 2026

A Novel Method: Super-selective Adrenal Venous Sampling
06:08

A Novel Method: Super-selective Adrenal Venous Sampling

Published on: September 15, 2017

Area of Science:

  • Radiology
  • Medical Imaging
  • Artificial Intelligence in Medicine

Background:

  • Adrenal abnormalities are frequently detected on computed tomography (CT) scans, occurring in over 5% of examinations.
  • Current methods for adrenal abnormality assessment using CT, such as region of interest (ROI) analysis, are operator-dependent and can be arbitrary.
  • There is a need for automated, objective methods to evaluate adrenal glands on CT.

Purpose of the Study:

  • To develop and validate an automated method for segmenting and analyzing adrenal glands on CT scans.
  • To assess the accuracy of automated histogram analysis following automatic adrenal gland segmentation for detecting adrenal abnormalities.
  • To overcome the limitations of manual ROI selection in CT-based adrenal gland evaluation.

Main Methods:

  • Utilized a random forest classification framework for pixel-wise classification of CT volumes (abdomen and pelvis) into right adrenal, left adrenal, and background.
  • Implemented automatic segmentation of the entire adrenal gland without human intervention.
  • Performed histogram analysis on the segmented adrenal gland regions to detect abnormalities.

Main Results:

  • The automated segmentation and histogram analysis method achieved a sensitivity of 80% and a specificity of 90% for detecting adrenal abnormalities.
  • The study analyzed 20 adrenal glands from volumetric CT datasets.
  • The combined approach demonstrated high accuracy in identifying adrenal gland abnormalities.

Conclusions:

  • Automated segmentation of the entire adrenal gland followed by histogram analysis is an accurate method for detecting adrenal abnormalities on CT scans.
  • This automated approach offers an objective and reproducible alternative to manual ROI-based analyses.
  • The findings support the potential of AI-driven tools in improving the diagnostic accuracy of adrenal imaging.