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Anatomy of the Brain: Ventricles

Anatomy of the Brain: Ventricles

There are hollow fluid-filled cavities known as ventricles deep inside the human brain. There are two lateral ventricles, one in each cerebral hemisphere, and each has three different projections — the anterior, inferior, and posterior horns visible from the lateral side. A thin membrane called the septum pellucidum separates the two lateral ventricles. The slender third ventricle in the diencephalon is connected to each lateral ventricle via a channel called the interventricular foramen.

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Related Experiment Video

Updated: Nov 1, 2025

3D Modeling of the Lateral Ventricles and Histological Characterization of Periventricular Tissue in Humans and Mouse

3D Modeling of the Lateral Ventricles and Histological Characterization of Periventricular Tissue in Humans and Mouse

Published on: May 19, 2015

Ventricle shape analysis using modified WKS for atrophy detection.

Jayaraman Thirumagal¹, Manjunatha Mahadevappa², Anup Sadhu³

¹School of Medical Science and Technology, IIT Kharagpur, Kharagpur, India.

Medical & Biological Engineering & Computing

|June 26, 2021

Summary

This summary is machine-generated.

This study introduces a novel spectral analysis algorithm using wave kernel signature for brain ventricle shape analysis. The automated method accurately classifies normal and atrophy subjects, aiding neurological disorder diagnosis.

Keywords:

Atrophy Enlargement Laplace-Beltrami operator Shape analysis Ventricles

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Last Updated: Nov 1, 2025

3D Modeling of the Lateral Ventricles and Histological Characterization of Periventricular Tissue in Humans and Mouse

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Published on: May 19, 2015

Morphological and Functional Assessment of the Right Ventricle Using 3D Echocardiography

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Published on: October 28, 2020

Evaluation of Left Ventricular Structure and Function using 3D Echocardiography

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Published on: October 28, 2020

Area of Science:

Neuroimaging
Biomarkers
Computational anatomy

Background:

Brain ventricles, filled with cerebrospinal fluid (CSF), are crucial biomarkers for neurological disorders.
Analyzing ventricle shape aids in diagnosing atrophy and CSF-related conditions.
Current diagnostic methods may lack efficiency or automation.

Purpose of the Study:

To introduce a novel spectral analysis algorithm for brain ventricle shape analysis.
To evaluate the algorithm's effectiveness in classifying normal versus atrophy subjects.
To compare the proposed method against existing shape analysis techniques.

Main Methods:

A spectral analysis algorithm based on wave kernel signature (WKS) was developed.
The WKS shape signature was applied to segmented brain ventricles from medical images.
Subjects were classified into normal and atrophy groups based on the derived shape signatures.

Main Results:

The proposed WKS algorithm achieved high classification accuracy (94-95%) for normal and atrophy subjects.
The method demonstrated superior performance compared to heat kernel signature, scale-invariant heat kernel signature, and spectral graph wavelet signature.
The algorithm proved to be simple, effective, automated, and time-efficient.

Conclusions:

The wave kernel signature-based spectral analysis is a highly accurate and efficient method for brain ventricle shape analysis.
This automated approach shows significant potential for improving the diagnosis of neurological disorders like atrophy.
The findings suggest WKS is a robust shape signature for neuroimaging analysis.