An Adaptive Semi-automated Integrated System for Multiple Sclerosis Lesion Segmentation in Longitudinal MRI Scans Based on a Convolutional Neural Network

2021 ◽  
pp. 256-265
Author(s):  
Andreas Georgiou ◽  
Christos P. Loizou ◽  
Andria Nicolaou ◽  
Marios Pantzaris ◽  
Constantinos S. Pattichis
Keyword(s):  
Neural Network ◽  
Multiple Sclerosis ◽  
Convolutional Neural Network ◽  
Integrated System ◽  
Multiple Sclerosis Lesion ◽  
Lesion Segmentation ◽  
Longitudinal Mri ◽  
Mri Scans

scholarly journals Improving automated multiple sclerosis lesion segmentation with a cascaded 3D convolutional neural network approach

NeuroImage ◽  
2017 ◽  
Vol 155 ◽  
pp. 159-168 ◽  
Author(s):  
Sergi Valverde ◽  
Mariano Cabezas ◽  
Eloy Roura ◽  
Sandra González-Villà ◽  
Deborah Pareto ◽  
...  
Keyword(s):  
Neural Network ◽  
Multiple Sclerosis ◽  
Convolutional Neural Network ◽  
Multiple Sclerosis Lesion ◽  
Network Approach ◽  
Lesion Segmentation ◽  
Neural Network Approach

scholarly journals Multiple Sclerosis Lesion Segmentation in Brain MRI Using Inception Modules Embedded in a Convolutional Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Shahab U. Ansari ◽  
Kamran Javed ◽  
Saeed Mian Qaisar ◽  
Rashad Jillani ◽  
Usman Haider
Keyword(s):  
Neural Network ◽  
Multiple Sclerosis ◽  
Convolutional Neural Network ◽  
Automatic Segmentation ◽  
Brain Mri ◽  
Brain Lesion ◽  
Similarity Index ◽  
Structural Similarity ◽  
Multiple Sclerosis Lesion ◽  
Lesion Segmentation

Multiple sclerosis (MS) is a chronic and autoimmune disease that forms lesions in the central nervous system. Quantitative analysis of these lesions has proved to be very useful in clinical trials for therapies and assessing disease prognosis. However, the efficacy of these quantitative analyses greatly depends on how accurately the MS lesions have been identified and segmented in brain MRI. This is usually carried out by radiologists who label 3D MR images slice by slice using commonly available segmentation tools. However, such manual practices are time consuming and error prone. To circumvent this problem, several automatic segmentation techniques have been investigated in recent years. In this paper, we propose a new framework for automatic brain lesion segmentation that employs a novel convolutional neural network (CNN) architecture. In order to segment lesions of different sizes, we have to pick a specific filter or size 3 × 3 or 5 × 5. Sometimes, it is hard to decide which filter will work better to get the best results. Google Net has solved this problem by introducing an inception module. An inception module uses 3 × 3 , 5 × 5 , 1 × 1 and max pooling filters in parallel fashion. Results show that incorporating inception modules in a CNN has improved the performance of the network in the segmentation of MS lesions. We compared the results of the proposed CNN architecture for two loss functions: binary cross entropy (BCE) and structural similarity index measure (SSIM) using the publicly available ISBI-2015 challenge dataset. A score of 93.81 which is higher than the human rater with BCE loss function is achieved.

scholarly journals Multi-branch convolutional neural network for multiple sclerosis lesion segmentation

NeuroImage ◽  
2019 ◽  
Vol 196 ◽  
pp. 1-15 ◽  
Author(s):  
Shahab Aslani ◽  
Michael Dayan ◽  
Loredana Storelli ◽  
Massimo Filippi ◽  
Vittorio Murino ◽  
...  
Keyword(s):  
Neural Network ◽  
Multiple Sclerosis ◽  
Convolutional Neural Network ◽  
Multiple Sclerosis Lesion ◽  
Lesion Segmentation

scholarly journals ALL-Net: Anatomical Information Lesion-wise Loss Function Integrated into Neural Network for Multiple Sclerosis Lesion Segmentation

2021 ◽  
pp. 102854
Author(s):  
Hang Zhang ◽  
Jinwei Zhang ◽  
Chao Li ◽  
Elizabeth M. Sweeney ◽  
Pascal Spincemaille ◽  
...  
Keyword(s):  
Neural Network ◽  
Multiple Sclerosis ◽  
Loss Function ◽  
Multiple Sclerosis Lesion ◽  
Lesion Segmentation ◽  
Anatomical Information

scholarly journals Brain Tumor Segmentation using K-Means Clustering and Detection using Convolutional Neural Network

2020 ◽  
Vol 9 (5) ◽  
pp. 1452-1455
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Brain Tumor ◽  
Convolutional Neural Network ◽  
Clustering Algorithm ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation ◽  
Mri Scans ◽  
Two Phases ◽  
Processing Techniques

This paper presents brain tumor detection and segmentation using image processing techniques. Convolutional neural networks can be applied for medical research in brain tumor analysis. The tumor in the MRI scans is segmented using the K-means clustering algorithm which is applied of every scan and the feed it to the convolutional neural network for training and testing. In our CNN we propose to use ReLU and Sigmoid activation functions to determine our end result. The training is done only using the CPU power and no GPU is used. The research is done in two phases, image processing and applying neural network.

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