Volume Preserving Brain Lesion Segmentation

2021 ◽  
pp. 60-69
Author(s):  
Yanlin Liu ◽  
Xiangzhu Zeng ◽  
Chuyang Ye
Keyword(s):  
Brain Lesion ◽  
Lesion Segmentation ◽  
Volume Preserving

Probabilistic Brain Lesion Segmentation in DT-MRI

2006 ◽  
Author(s):  
Gady Agam ◽  
Daniel Weiss ◽  
Mandar Soman ◽  
Konstantinos Arfanakis
Keyword(s):  
Brain Lesion ◽  
Lesion Segmentation

Energy functional driven by multiple features for brain lesion segmentation

2022 ◽  
Author(s):  
Lingling Fang ◽  
Yibo Yao ◽  
Lirong Zhang ◽  
Xin Wang ◽  
Qile Zhang
Keyword(s):  
Brain Lesion ◽  
Energy Functional ◽  
Lesion Segmentation ◽  
Multiple Features

Improved Brain Lesion Segmentation with Anatomical Priors from Healthy Subjects

2021 ◽  
pp. 186-195
Author(s):  
Chenghao Liu ◽  
Xiangzhu Zeng ◽  
Kongming Liang ◽  
Yizhou Yu ◽  
Chuyang Ye
Keyword(s):  
Healthy Subjects ◽  
Brain Lesion ◽  
Lesion Segmentation ◽  
Anatomical Priors

scholarly journals RFDCR: Automated brain lesion segmentation using cascaded random forests with dense conditional random fields

NeuroImage ◽  
2020 ◽  
Vol 211 ◽  
pp. 116620 ◽  
Author(s):  
Gaoxiang Chen ◽  
Qun Li ◽  
Fuqian Shi ◽  
Islem Rekik ◽  
Zhifang Pan
Keyword(s):  
Random Forests ◽  
Random Fields ◽  
Conditional Random Fields ◽  
Brain Lesion ◽  
Lesion Segmentation

Semi-Supervised Brain Lesion Segmentation Using Training Images with and Without Lesions

2020 ◽  
Author(s):  
Chenghao Liu ◽  
Fengqian Pang ◽  
Yanlin Liu ◽  
Kongming Liang ◽  
Xiuli Li ◽  
...  
Keyword(s):  
Brain Lesion ◽  
Lesion Segmentation ◽  
Training Images

scholarly journals Unsupervised brain lesion segmentation from MRI using a convolutional autoencoder

2019 ◽  
Author(s):  
Hans E. Atlason ◽  
Askell Love ◽  
Sigurdur Sigurdsson ◽  
Vilmundur Gudnason ◽  
Lotta M. Ellingsen
Keyword(s):  
Brain Lesion ◽  
Lesion Segmentation ◽  
Convolutional Autoencoder

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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