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

Medical & Biological Engineering & Computing
Ehab EssaM Z Rashad

Abstract

The segmentation of the lesion plays a core role in diagnosis and monitoring of multiple sclerosis (MS). Magnetic resonance imaging (MRI) is the most frequent image modality used to evaluate such lesions. Because of the massive amount of data, manual segmentation cannot be achieved within a sensible time that restricts the usage of accurate quantitative measurement in clinical practice. Therefore, the need for effective automated segmentation techniques is critical. However, a large spatial variability between the structure of brain lesions makes it more challenging. Recently, convolutional neural network (CNN), in particular, the region-based CNN (R-CNN), have attained tremendous progress within the field of object recognition because of its ability to learn and represent features. CNN has proven a last-breaking performance in various fields, such as object recognition, and has also gained more attention in brain imaging, especially in tissue and brain segmentation. In this paper, an automated technique for MS lesion segmentation is proposed, which is built on a 3D patch-wise R-CNN. The proposed system includes two stages: first, segmenting MS lesions in T2-w and FLAIR sequences using R-CNN, then an adaptive neuro-fuzzy infere...Continue Reading

References

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Apr 11, 2019·International Journal for Numerical Methods in Biomedical Engineering·Ehab EssaXianghua Xie

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Citations

Feb 10, 2021·Biomedicines·Meredith A JonesLacey R McNally
Aug 31, 2021·Frontiers in Immunology·Faezeh MoazamiVassili Soumelis

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Methods Mentioned

BETA
feature extraction

Software Mentioned

Fast R - CNN
FSL
R
Fast R CNN
ANFIS
MiniBatchSize
CNN
T2
T1

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