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Related Experiment Video

Updated: Sep 13, 2025

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

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

Published on: January 7, 2019

Deep learning-driven brain tumor classification and segmentation using non-contrast MRI.

Nan-Han Lu^1,2, Yung-Hui Huang³, Kuo-Ying Liu⁴

¹Department of Radiology, E-DA Cancer Hospital, I-Shou University, No. 21, Yida Road, Jiao- Su Village, Yan-Chao District, Kaohsiung, 82445, Taiwan. leunanhan@seed.net.tw.

Scientific Reports

|July 31, 2025

Summary

This summary is machine-generated.

Deep learning models using combined MRI scans (T1w, T2w, and average) significantly improve brain tumor diagnosis accuracy. This approach achieved 98.3% accuracy in classification and 0.937 Dice score in segmentation.

Keywords:

Artificial intelligence Brain MRI Convolutional neural networks (CNNs)Deep learning Fully convolutional networks (FCNs)Tumor classification Tumor segmentation

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Area of Science:

Medical Imaging
Artificial Intelligence
Radiology

Background:

Accurate brain tumor diagnosis is crucial for effective treatment.
Current MRI analysis can be time-consuming and subjective.
Deep learning offers potential for automated and improved diagnostic accuracy.

Purpose of the Study:

To enhance the accuracy and efficiency of brain tumor diagnosis using deep learning.
To evaluate the impact of multichannel MRI input fusion on diagnostic performance.
To assess deep learning models for both brain tumor classification and segmentation.

Main Methods:

Collected MRI data from 203 subjects (100 normal, 103 with tumors).
Created three-channel RGB inputs by fusing non-contrast T1-weighted (T1w), T2-weighted (T2w) images, and their average.
Employed Convolutional Neural Networks (CNNs) for classification and Fully Convolutional Networks (FCNs) for segmentation.

Main Results:

RGB fusion of MRI sequences significantly improved model performance.
Darknet53 model achieved 98.3% accuracy for tumor classification.
ResNet50 model attained a mean Dice score of 0.937 for tumor segmentation.

Conclusions:

Multichannel input fusion and appropriate model selection enhance deep learning-based brain tumor analysis.
The developed deep learning approach shows significant promise for improving diagnostic accuracy and efficiency.
This method has potential for future development into clinical decision-support tools in radiology.