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Neuro-fuzzy patch-wise R-CNN for multiple sclerosis segmentation.

Ehab Essa¹, Doaa Aldesouky², Sherif E Hussein³

¹Computer Science Department, Faculty of Computers and Information, Mansoura University, Mansoura, Dakahlia Governorate, Egypt. ehab_essa@mans.edu.eg.

Medical & Biological Engineering & Computing

|July 19, 2020

Summary

This summary is machine-generated.

Automated segmentation of multiple sclerosis (MS) lesions using 3D region-based convolutional neural networks (R-CNN) and adaptive neuro-fuzzy inference systems (ANFIS) improves diagnostic accuracy. This technique effectively fuses MRI data for precise lesion identification.

Keywords:

Deep learning MS segmentation Neuro-fuzzy R-CNN

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Published on: September 25, 2019

Area of Science:

Medical Imaging
Artificial Intelligence
Neuroscience

Background:

Accurate segmentation of multiple sclerosis (MS) lesions is crucial for diagnosis and monitoring.
Manual segmentation is time-consuming and impractical for large datasets.
Brain lesion variability presents challenges for automated segmentation.

Purpose of the Study:

To develop an automated technique for MS lesion segmentation using advanced deep learning.
To improve the accuracy and efficiency of MS lesion quantification in clinical practice.
To fuse information from multiple MRI sequences for robust segmentation.

Main Methods:

A two-stage approach utilizing 3D patch-wise region-based convolutional neural networks (R-CNN).
Segmentation of MS lesions in T2-weighted and FLAIR MRI sequences using R-CNN.
Fusion of segmentation results from different modalities using an adaptive neuro-fuzzy inference system (ANFIS).

Main Results:

The proposed method achieved competitive results on the MICCAI2008 MS challenge dataset.
An average total score of 83.25 and an average sensitivity of 61.8% were obtained on the testing set.
The system demonstrated effective fusion of FLAIR and T2-weighted MRI data for lesion segmentation.

Conclusions:

The developed automated technique shows significant promise for accurate and efficient MS lesion segmentation.
The combination of R-CNN and ANFIS offers a robust solution for complex brain lesion analysis.
This approach can aid in the clinical management and research of multiple sclerosis.