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Magnetic Resonance Imaging

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Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...

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Related Experiment Video

Updated: Jun 9, 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

Automated Lesion and Feature Extraction Pipeline for Brain MRIs with Interpretability.

Reza Eghbali^1,2, Pierre Nedelec³, David Weiss⁴

¹Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, USA. eghbali@berkeley.edu.

Neuroinformatics

|January 9, 2025

Summary

This summary is machine-generated.

This study presents the Automated Lesion and Feature Extraction (ALFE) pipeline, an open-source tool for brain MRI analysis. ALFE generates detailed lesion segmentations and features for quantitative analysis and machine learning applications.

Keywords:

MRI pipeline Neuroradiology Radiomics

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

Published on: September 25, 2019

Area of Science:

Medical Imaging
Artificial Intelligence
Neuroscience

Background:

Quantitative analysis of brain MRIs is crucial for diagnosing and monitoring neurological conditions.
Existing tools for lesion segmentation and feature extraction can be complex and lack flexibility.
There is a need for automated, customizable pipelines that integrate with clinical workflows.

Purpose of the Study:

To introduce the Automated Lesion and Feature Extraction (ALFE) pipeline, an open-source tool for brain MRI analysis.
To demonstrate ALFE's ability to perform automated anatomical and lesion segmentation.
To highlight ALFE's capability in extracting human-interpretable imaging features for clinical and machine learning applications.

Main Methods:

Development of an open-source, Python-based pipeline named ALFE.
Implementation of a decoupled design allowing customization of image processing, registration, and AI segmentation modules.
Modeling the pipeline after established neuroradiology workflows.

Main Results:

ALFE successfully generates accurate anatomical and lesion segmentations from brain MR images.
The pipeline extracts quantitative, human-interpretable imaging features describing brain lesions.
Case studies demonstrate the pipeline's utility in real-world scenarios.

Conclusions:

The ALFE pipeline offers a flexible and automated solution for brain MRI analysis.
ALFE facilitates quantitative analysis and machine learning applications by providing standardized lesion features.
This open-source tool has the potential to advance clinical research and practice in neuroradiology.