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Imaging Studies III: Computed Tomography

Imaging Studies III: Computed Tomography

DefinitionComputed Tomography (CT) of the genitourinary (GU) tract is a non-invasive imaging modality that utilizes X-rays and computer processing to generate detailed cross-sectional images of the urinary system, encompassing the kidneys, ureters, bladder, and adjacent structures such as the adrenal glands.PurposeCT scans of the GU tract serve several diagnostic and therapeutic purposes, including:Diagnosis of Urinary Tract Diseases: Detects kidney stones, tumors, cysts, and congenital...

Imaging Studies IV: Magnetic Resonance Imaging

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Introduction:Magnetic Resonance Imaging, or MRI, can include a specialized imaging technique of the urinary system known as Magnetic Resonance Urography (MRU). This radiation-free technique uses strong magnetic fields and radio waves to produce detailed images with the help of a computer. MRU is particularly effective for visualizing fluid-filled structures like the kidneys, ureters, and bladder.Applications of MRI in the Genitourinary SystemKidneys and Ureters: MRI detects tumors, cysts,...

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Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

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Multivariate Brain Tumor Detection in 3D-MRI Images Using Optimised Segmentation and Unified Classification Model.

¹Department of Electronics and Communication Engineering, Sri Muthukumaran Institute of Technology, Chennai, Tamil Nadu, India.

Journal of Evaluation in Clinical Practice

|November 20, 2024

Summary

This study introduces a novel Duo-step optimised Pyramidal SegNet and 3D brain Unified NN for improved brain tumor segmentation and classification. The proposed methods significantly reduce segmentation errors and enhance detection rates in 3D MRI analysis.

Keywords:

artificial intelligence (AI)brain tumor magnetic resonance imaging (MRI)optimisation segmentation tumor detection

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Multicolor 3D Printing of Complex Intracranial Tumors in Neurosurgery

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Published on: January 11, 2020

Area of Science:

Medical Imaging
Artificial Intelligence in Medicine
Neuro-oncology

Background:

3D Magnetic Resonance Imaging (3D-MRI) is crucial for brain tumor diagnosis and treatment planning.
Existing segmentation techniques struggle with accuracy due to issues in initial contour point extraction and overlapping tissue intensities.
Accurate classification of brain tumors is hindered by challenges in extracting contextual and symmetric features.

Purpose of the Study:

To develop an advanced segmentation method for precise brain tumor localization and delineation.
To propose a novel classification approach for improved detection rates of multivariate brain tumors.
To minimize segmentation errors and enhance the overall diagnostic accuracy in brain tumor analysis.

Main Methods:

A Duo-step optimised Pyramidal SegNet with a multiscale contrast convolutional attention module for improved contrast and edge extraction.
Duo-step darning needle optimization and pyramidal level set segmentation with Sobel edge operator for accurate tumor region extraction.
A 3D brain Unified Neural Network (NN) employing an adaptive multi-layer deep unified encoder to extract 3D contextual and symmetric features.

Main Results:

The proposed segmentation method effectively minimizes errors by avoiding overlapped tissue intensity distributions.
The 3D brain Unified NN demonstrated high detection rates by extracting crucial 3D contextual and symmetric features.
Evaluated on BraTS2020 and Brain Tumor Detection 2020 datasets, the model achieved high precision, recall, and accuracy.

Conclusions:

The novel Duo-step optimised Pyramidal SegNet and 3D brain Unified NN significantly outperform existing techniques in brain tumor segmentation and classification.
The proposed methods offer enhanced precision, recall, and accuracy, improving diagnostic capabilities for brain tumors.
This research provides a robust framework for more accurate and reliable 3D-MRI based brain tumor analysis.