Using AI-automated segmentation to analyze aging-related volume changes in the brain and cerebrospinal fluid
Introduction:
In the fast-paced world of healthcare, deep learning methods are gaining popularity in the assessment of disease-specific brain atrophy from CT and MRI images. This advancement marks the onset of a new era in clinical neuroimaging. In a recent study, researchers explored the effects of aging and gender differences in volumes and volume ratios of regional brain and cerebrospinal fluid (CSF) spaces in healthy volunteers. By utilizing an artificial intelligence-based automatic region segmentation application, the 3D T1-weighted brain MRI was accurately measured within a minute. Key findings include the linear decrease of cortical gray matter with aging, the maintenance of volume in subcortical gray matter, and the gradual increase in CSF volume. To learn more about this groundbreaking research, click [here](https://link.springer.com/article/10.1007/s00330-023-09632-x).
Full Article: Using AI-automated segmentation to analyze aging-related volume changes in the brain and cerebrospinal fluid
Deep Learning Methods in Assessing Brain Atrophy from CT and MRI Images: A Breakthrough in Clinical Neuroimaging
A new era in clinical neuroimaging is on the horizon as deep learning methods for quantitatively assessing disease-specific brain atrophy from CT and MRI images gain popularity. In this article, we explore the effects of aging and gender differences in volumes and volume ratios of regional brain and cerebrospinal fluid (CSF) spaces in healthy volunteers, using the Brain Subregion Analysis application on a SYNAPSE 3D workstation – the most popular workstation in Japan.
Understanding Brain Changes Using Artificial Intelligence-Based Applications
With the help of this artificial intelligence-based automatic region segmentation application, the 3D T1-weighted brain MRI is automatically segmented into 21 brain subregions and 5 CSF subregions, providing accurate measurements of volume and volume ratio for each region in under 1 minute. The findings reveal interesting patterns in the brain’s structure and dimensions as humans age.
Effects of Aging on Brain Volume and Cerebrospinal Fluid Spaces
In a healthy brain, cortical gray matter decreases linearly with aging starting from the 20s, while cerebral white matter increases until the 40s and then begins to decrease in volume from the 50s onwards. Interestingly, subcortical gray matter, such as the hippocampus, maintains its volume throughout an individual’s lifespan. As brain volume decreases with age, the intracranial cerebrospinal fluid increases. For example, the CSF volume in a healthy individual in their 20s is approximately 265 mL, accounting for less than 20% of the intracranial volume. However, the CSF volume increases by about 3 mL per year, surpassing 450 mL and accounting for over 30% of the intracranial volume in individuals aged 80 years or more.
Understanding the Role of Ventricular Enlargement in Chronic Hydrocephalus
Another significant finding is the linear increase in the subarachnoid space covering the brain surface starting from the 20s. In contrast, the ventricle inside the brain maintains a volume of around 20 mL (less than 2% of the intracranial volume) until the 60s, after which it starts to increase. This post-60s ventricular enlargement may be a key factor in causing chronic hydrocephalus in the elderly.
Key Points in Brain and Cerebrospinal Fluid Analysis
– An artificial intelligence-based application allows automatic segmentation of the brain and CSF spaces.
– Total subarachnoid spaces increase linearly with aging, while total ventricle volume remains around 20 mL until the 60s and increases thereafter.
– Cortical gray matter gradually decreases with aging, subcortical gray matter maintains its volume, and cerebral white matter slightly increases until the 40s before decreasing.
To read the full article titled “Aging-related volume changes in the brain and cerebrospinal fluid using artificial intelligence-automated segmentation”, click [here](https://link.springer.com/article/10.1007/s00330-023-09632-x).
About the Authors
This article was written by Shigeki Yamada, Tomohiro Otani, Satoshi Ii, Hiroto Kawano, Kazuhiko Nozaki, Shigeo Wada, Marie Oshima, and Yoshiyuki Watanabe. Shigeki Yamada is associated with the Department of Neurosurgery at Nagoya City University Graduate School of Medical Science, Nagoya, Japan, as well as the Interfaculty Initiative in Information Studies and Institute of Industrial Science at The University of Tokyo, Tokyo, Japan.
In Conclusion
The emergence of deep learning methods for assessing brain atrophy marks a significant breakthrough in clinical neuroimaging. Through automatic segmentation and analysis of CT and MRI images, researchers can gain valuable insights into the effects of aging and gender differences on brain volume and cerebrospinal fluid spaces. These findings have the potential to enhance our understanding of age-related brain changes and neurological disorders.
Summary: Using AI-automated segmentation to analyze aging-related volume changes in the brain and cerebrospinal fluid
Deep learning methods are being used to assess disease-specific brain atrophy from CT and MRI images. In a study on healthy volunteers, the effects of aging and gender differences on brain and cerebrospinal fluid (CSF) volumes and ratios were investigated. Using an artificial intelligence-based application, the brain MRI was automatically segmented, and the volume and volume ratio of each region were accurately measured. The study found that the cortical gray matter decreases with aging, while the subcortical gray matter and cerebral white matter show different patterns of change. The results provide valuable insights into the aging brain and CSF dynamics.
