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Related Experiment Video

Updated: Mar 2, 2026

Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly

Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly

Published on: April 14, 2014

A level set method for multiple sclerosis lesion segmentation.

Yue Zhao¹, Shuxu Guo¹, Min Luo²

¹School of Electronic Engineering, Jilin University, Changchun, Jilin, China.

Magnetic Resonance Imaging

|May 20, 2017

Summary

This summary is machine-generated.

This study introduces a novel level set method for segmenting multiple sclerosis (MS) lesions in FLAIR images. The method accurately identifies lesions despite image intensity variations, improving segmentation accuracy.

Keywords:

Intensity inhomogeneity Lesion segmentation Level set MRI

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Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Published on: September 25, 2019

Area of Science:

Medical imaging analysis
Computational neuroscience
Biomedical image processing

Background:

Accurate segmentation of multiple sclerosis (MS) lesions is crucial for diagnosis and monitoring.
FLAIR images are commonly used for MS lesion detection but are susceptible to intensity inhomogeneities.
Existing segmentation methods struggle with bias field correction, impacting accuracy.

Purpose of the Study:

To develop an advanced level set method for robust MS lesion segmentation from FLAIR images.
To address challenges posed by intensity inhomogeneities and bias fields in medical scans.
To improve the precision and computational efficiency of MS lesion segmentation.

Main Methods:

A three-phase level set formulation for simultaneous segmentation and bias field estimation was initially proposed.
A computationally efficient two-phase level set formulation was derived for segmenting MS lesions and normal tissue.
The method precisely delineates object boundaries while correcting for intensity variations.

Main Results:

The proposed two-phase level set method demonstrated superior segmentation accuracy compared to existing state-of-the-art techniques.
The method effectively handles intensity inhomogeneities, leading to more reliable lesion identification.
Experimental results validated the method's advantages in segmenting MS lesions and normal tissue regions.

Conclusions:

The developed level set method offers a significant advancement in MS lesion segmentation from FLAIR images.
The approach provides accurate and robust segmentation, even in the presence of challenging image artifacts.
This method holds promise for improved clinical assessment and research in multiple sclerosis.