scholarly journals Towards Clinical Diagnosis: Automated Stroke Lesion Segmentation on Multi-Spectral MR Image Using Convolutional Neural Network

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 57006-57016 ◽  
Author(s):  
Zhiyang Liu ◽  
Chen Cao ◽  
Shuxue Ding ◽  
Zhiang Liu ◽  
Tong Han ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Clinical Diagnosis ◽  
Lesion Segmentation ◽  
Mr Image ◽  
Stroke Lesion

scholarly journals Automatic post-stroke lesion segmentation on MR images using 3D residual convolutional neural network

2020 ◽  
Vol 27 ◽  
pp. 102276
Author(s):  
Naofumi Tomita ◽  
Steven Jiang ◽  
Matthew E. Maeder ◽  
Saeed Hassanpour
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Lesion Segmentation ◽  
Mr Images ◽  
Post Stroke ◽  
Stroke Lesion

scholarly journals NIMG-61. USE OF TEXTURAL RADIOMIC MAPS IN A 3D CONVOLUTIONAL NEURAL NETWORK FRAMEWORK CAN AUGMENT GLIOMA LESION SEGMENTATION

2017 ◽  
Vol 19 (suppl_6) ◽  
pp. vi156-vi156
Author(s):  
Ayush Karnawat ◽  
Prateek Prasanna ◽  
Anant Madabhushi ◽  
Pallavi Tiwari
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Lesion Segmentation

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.

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