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Related Experiment Video

Updated: Oct 1, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

A multi-sequences MRI deep framework study applied to glioma classfication.

Matthieu Coupet^1,2, Thierry Urruty^1,2, Teerapong Leelanupab³

¹XLIM Laboratory, University of Poitiers, UMR CNRS 7252, Poitiers, France.

Multimedia Tools and Applications

|March 7, 2022

Summary

This summary is machine-generated.

This study introduces a computer-aided system using deep learning and MRI scans to help diagnose brain gliomas. It identifies optimal model-MRI sequence combinations for accurate glioma detection.

Keywords:

Deep leaning model Glioma classification MRI Model interpretability Multi-sequences

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Last Updated: Oct 1, 2025

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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:

Neuroscience
Medical Imaging
Artificial Intelligence

Background:

Glioma is a significant central nervous system tumor, impacting cancer statistics for both men and women.
Magnetic Resonance Imaging (MRI) is crucial for glioma diagnosis, utilizing multi-sequence scans based on pathology severity.

Purpose of the Study:

To develop a computer-aided system to assist medical experts in diagnosing brain gliomas.
To evaluate the performance of various pre-trained deep learning models across different MRI sequences for glioma classification.

Main Methods:

A supervised learning framework employing convolutional neural networks and transfer learning techniques.
Investigated the efficacy of different deep learning models on multi-sequence MRI data from the BraTS dataset.
Performed visual analysis of deep features to understand model-MRI sequence interactions.

Main Results:

Identified optimal combinations of deep learning models and MRI sequences for distinguishing between healthy brains and those with glioma.
Demonstrated the potential of AI in enhancing glioma diagnostic accuracy.
Provided insights into the relationship between MRI sequences and model performance through interpretability analysis.

Conclusions:

The proposed computer-aided system shows promise in assisting experts with glioma diagnosis.
Specific model-MRI sequence pairings are superior for classifying glioma.
Interpretability analysis aids in understanding AI-driven diagnostic decisions in neuro-oncology.