scholarly journals Review of MRI-based Brain Tumor Image Segmentation Using Deep Learning Methods

2016 ◽  
Vol 102 ◽  
pp. 317-324 ◽  
Author(s):  
Ali Işın ◽  
Cem Direkoğlu ◽  
Melike Şah
Keyword(s):  
Image Segmentation ◽  
Deep Learning ◽  
Brain Tumor ◽  
Learning Methods

scholarly journals Region-of-Interest-Based Cardiac Image Segmentation with Deep Learning

2021 ◽  
Vol 11 (4) ◽  
pp. 1965
Author(s):  
Raul-Ronald Galea ◽  
Laura Diosan ◽  
Anca Andreica ◽  
Loredana Popa ◽  
Simona Manole ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Deep Learning ◽  
Diagnostic System ◽  
Region Of Interest ◽  
General Purpose ◽  
Medical Image Segmentation ◽  
Dice Similarity Coefficient ◽  
Learning Methods ◽  
Study Results ◽  
Whole Heart

Despite the promising results obtained by deep learning methods in the field of medical image segmentation, lack of sufficient data always hinders performance to a certain degree. In this work, we explore the feasibility of applying deep learning methods on a pilot dataset. We present a simple and practical approach to perform segmentation in a 2D, slice-by-slice manner, based on region of interest (ROI) localization, applying an optimized training regime to improve segmentation performance from regions of interest. We start from two popular segmentation networks, the preferred model for medical segmentation, U-Net, and a general-purpose model, DeepLabV3+. Furthermore, we show that ensembling of these two fundamentally different architectures brings constant benefits by testing our approach on two different datasets, the publicly available ACDC challenge, and the imATFIB dataset from our in-house conducted clinical study. Results on the imATFIB dataset show that the proposed approach performs well with the provided training volumes, achieving an average Dice Similarity Coefficient of the whole heart of 89.89% on the validation set. Moreover, our algorithm achieved a mean Dice value of 91.87% on the ACDC validation, being comparable to the second best-performing approach on the challenge. Our approach provides an opportunity to serve as a building block of a computer-aided diagnostic system in a clinical setting.

Advancements of MRI-Based Brain Tumor Segmentation from Traditional to Recent Trends- A Review

2021 ◽  
Vol 17 ◽  
Author(s):  
Padmapriya Thiyagarajan ◽  
Sriramakrishnan Padmanaban ◽  
Kalaiselvi Thiruvenkadam ◽  
Somasundaram Karuppanagounder
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Brain Tumor ◽  
Human Head ◽  
Medical Community ◽  
Tumor Segmentation ◽  
Normal Brain ◽  
Learning Methods ◽  
Brain Tumor Segmentation ◽  
Mri Scans

Background: Among the brain-related diseases, brain tumor segmentation on magnetic resonance imaging (MRI) scans is one of the highly focused research domains in the medical community. Brain tumor segmentation is a very challenging task due to its asymmetric form and uncertain boundaries. This process segregates the tumor region into the active tumor, necrosis and edema from normal brain tissues such as white matter (WM), grey matter (GM), and cerebrospinal fluid (CSF). Introduction: The proposed paper analyzed the advancement of brain tumor segmentation from conventional image processing techniques, to deep learning through machine learning on MRI of human head scans. Method: State-of-the-art methods of these three techniques are investigated, and the merits and demerits are discussed. Results: The prime motivation of the paper is to instigate the young researchers towards the development of efficient brain tumor segmentation techniques using conventional and recent technologies. Conclusion: The proposed analysis concluded that the conventional and machine learning methods were mostly applied for brain tumor detection, whereas deep learning methods were good at tumor substructures segmentation.

scholarly journals Overview of Multi-Modal Brain Tumor MR Image Segmentation

Healthcare ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 1051
Author(s):  
Wenyin Zhang ◽  
Yong Wu ◽  
Bo Yang ◽  
Shunbo Hu ◽  
Liang Wu ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Brain Tumor ◽  
Brain Tumors ◽  
Brain Diseases ◽  
Learning Methods ◽  
Brain Images ◽  
Advantages And Disadvantages ◽  
Segmentation Methods ◽  
Magnetic Resonance Imaging Mri ◽  
Tumor Mri

The precise segmentation of brain tumor images is a vital step towards accurate diagnosis and effective treatment of brain tumors. Magnetic Resonance Imaging (MRI) can generate brain images without tissue damage or skull artifacts, providing important discriminant information for clinicians in the study of brain tumors and other brain diseases. In this paper, we survey the field of brain tumor MRI images segmentation. Firstly, we present the commonly used databases. Then, we summarize multi-modal brain tumor MRI image segmentation methods, which are divided into three categories: conventional segmentation methods, segmentation methods based on classical machine learning methods, and segmentation methods based on deep learning methods. The principles, structures, advantages and disadvantages of typical algorithms in each method are summarized. Finally, we analyze the challenges, and suggest a prospect for future development trends.

Biomedical Image Segmentation by Deep Learning Methods

2021 ◽  
pp. 131-154
Author(s):  
K. Anita Davamani ◽  
C.R. Rene Robin ◽  
S. Amudha ◽  
L. Jani Anbarasi
Keyword(s):  
Image Segmentation ◽  
Deep Learning ◽  
Learning Methods ◽  
Biomedical Image

scholarly journals NEURAL NETWORK AND CONVOLUTIONAL ALGORITH TO EXTRACT SHAPES BY E-MEDICUS APPLICATION

2018 ◽  
Vol 8 (3) ◽  
pp. 39-42
Author(s):  
Tomasz Rymarczyk ◽  
Barbara Stefaniak ◽  
Przemysław Adamkiewicz
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Image Segmentation ◽  
Deep Learning ◽  
Level Set Method ◽  
Level Set ◽  
Arbitrary Number ◽  
Medical Images ◽  
Learning Methods ◽  
Segmentation Problem

The solution shows the architecture of the system collecting and analyzing data. There was tried to develop algorithms to image segmentation. These algorithms are needed to identify arbitrary number of phases for the segmentation problem. With the use of algorithms such as the level set method, neural networks and deep learning methods, it can obtain a quicker diagnosis and automatically marking areas of the interest region in medical images.

scholarly journals A Dual Autoencoder and Singular Value Decomposition Based Feature Optimization for the Detection of Brain Tumor from MRI Images

2020 ◽  
Author(s):  
Aswani K ◽  
Menaka D
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Deep Learning ◽  
Brain Tumor ◽  
Tumor Segmentation ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Medical Standards ◽  
Latent Space ◽  
Feature Optimization

Abstract IntroductionThe brain tumor is the growth of abnormal cells inside the brain. These cells can be grown into malignant or benign tumors. Segmentation of tumor from MRI images using image processing techniques started decades back. Image processing based brain tumor segmentation can be divided in to three categories conventional image processing methods, Machine Learning methods and Deep Learning methods. Conventional methods lacks the accuracy in segmentation due to complex spatial variation of tumor. Machine Learning methods stand as a good alternative to conventional methods. Methods like SVM, KNN, Fuzzy and a combination of either of these provide good accuracy with reasonable processing speed. The difficulty in processing the various feature extraction methods and maintain accuracy as per the medical standards still exist as a limitation for machine learning methods. In Deep Learning features are extracted automatically in various stages of the network and maintain accuracy as per the medical standards. Huge database requirement and high computational time is still poses a problem for deep learning. MethodTo overcome the limitations specified above we propose an unsupervised dual autoencoder with latent space optimization here. The model require only normal MRI images for its training thus reducing the huge tumor database requirement. With a set of normal class data, an autoencoder can reproduce the feature vector into an output layer. This trained autoencoder works well with normal data while it fails to reproduce an anomaly to the output layer. But a classical autoencoder suffer due to poor latent space optimization. The Latent space loss of classical autoencoder is reduced using an auxiliary encoder along with the feature optimization based on Singular value Decomposition (SVD). The patches used for training are not traditional square patches but we took both horizontal and vertical patches to keep both local and global appearance features on the training set. An Autoencoder is applied separately for learning both horizontal and vertical patches. While training a logistic sigmoid transfer function is used for both encoder and decoder parts. SGD optimizer is used for optimization with an initial learning rate of .001 and the maximum epochs used are 4000. The network is trained in MATLAB 2018a with a processor capacity of 3.7 GHz with NVIDIA GPU and 16 GB of RAM.ResultsThe results are obtained using a patch size of 16x64, 64x16 for horizontal and vertical patches respectively. In Glioma images tumor is not grown from a point rather it spreads randomly. Region filling and connectivity operations are performed to get the final tumor segmentation. Overall the method segments Meningioma better than Gliomas. Three evaluation metrics are considered to measure the performance of the proposed system such as Dice Similarity Coefficient (DSC), Positive Predictive Value (PPV), and Sensitivity.ConclusionAn unsupervised method for the segmentation of brain tumor from MRI images is proposed here. The proposed dual autoencoder with SVD based feature optimization reduce the latent space loss in the classical autoencoder. The proposed method have advantages in computational efficiency, no need of huge database requirement and better accuracy than machine learning methods. The method is compared Machine Learning methods Like SVM, KNN and supervised deep learning methods like CNN and commentable results are obtained.

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