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Automated multiple sclerosis lesion segmentation from 3D-FLAIR MRI using R2AUNet: A deep learning approach with
Aref Andishgar1, Maziyar Rismani1, Zahra Mohammadi2
1Trauma Research Center, Shahid Rajaee (Emtiaz) Trauma Hospital, Department of Neurosurgery, Shiraz University of Medical Sciences, Shiraz, , Iran; Clinical Neurology Research Center, Shiraz University of Medical Sciences, Shiraz, Iran; Medical Imaging Research Center, Shiraz University of Medical Sciences, Shiraz, Iran.
Background:
Multiple sclerosis (MS) is a progressive neurological disorder marked by demyelinating lesions in the central nervous system. While MRI is essential for MS diagnosis, manual lesion segmentation is time-consuming and prone to variability. This study develops an automated deep learning (DL) approach for MS lesion segmentation using 3D-FLAIR MRI.
Method:
The R2AUNet DL model, incorporating recurrent residual blocks and attention gates within a 3D U-Net framework, was developed. The dataset included 112 MRI scans from 95 MS patients, collected between 2019 and 2023 at Shiraz Picture Archiving and Communication System (PACS). All patients had a confirmed MS diagnosis based on clinical assessments and the 2017 McDonald criteria, with manual lesion segmentations from expert neurologists as ground truth. The model was trained using an optimized preprocessing pipeline (brain extraction, bias field correction, intensity normalization, and 3D patch extraction). Dice Similarity Coefficient (DSC), specificity, sensitivity, F1-score, and precision were used to evaluate performance.
Result:
The proposed model achieved a DSC of 0.768 on the test set, with an accuracy of 0.998, sensitivity of 0.765, and precision of 0.825, demonstrating robust segmentation performance. Qualitative analysis confirmed the model's ability to accurately delineate lesions while reducing false positives.
Conclusion:
R2AUNet provides a reliable and automated solution for MS lesion segmentation from 3D-FLAIR MRI, offering high accuracy and efficiency. This study demonstrates that an automated DL model can achieve accurate MS lesion segmentation using only 3D-FLAIR MRI, eliminating the need for multiple MRI sequences. This simplifies data acquisition, reduces computational complexity, and improves accessibility, particularly in resource-limited settings.
Insights
A new deep learning model, R2AUNet, automates multiple sclerosis lesion segmentation using 3D-FLAIR MRI. This approach enhances diagnostic accuracy and efficiency, simplifying the process for clinicians.
Area of Science:
- Neurology
- Medical Imaging
- Artificial Intelligence
Background:
- Multiple sclerosis (MS) is a neurological disorder characterized by demyelinating lesions.
- Manual segmentation of these lesions in MRI scans is time-consuming and variable.
- Automated methods are needed to improve the efficiency and consistency of MS lesion segmentation.
Purpose of the Study:
- To develop and evaluate an automated deep learning (DL) model for segmenting MS lesions.
- To utilize 3D-FLAIR MRI sequences for lesion segmentation.
- To assess the performance of the DL model against expert manual segmentations.
Main Methods:
- Developed the R2AUNet DL model, a 3D U-Net with recurrent residual blocks and attention gates.
- Trained the model on 112 3D-FLAIR MRI scans from 95 MS patients.
- Employed an optimized preprocessing pipeline and evaluated performance using Dice Similarity Coefficient (DSC), accuracy, sensitivity, and precision.
Main Results:
- The R2AUNet model achieved a DSC of 0.768 on the test set.
- The model demonstrated high accuracy (0.998), sensitivity (0.765), and precision (0.825).
- Qualitative analysis confirmed accurate lesion delineation and reduced false positives.
Conclusions:
- R2AUNet offers a reliable and automated solution for MS lesion segmentation from 3D-FLAIR MRI.
- The DL model achieves high accuracy and efficiency, simplifying data acquisition.
- This automated approach improves accessibility, especially in resource-limited settings.
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