A Brain MR Images Segmentation Method Based on SOM Neural Network

2007 ◽  
Author(s):  
Dan Tian ◽  
Linan Fan
Keyword(s):  
Neural Network ◽  
Segmentation Method ◽  
Mr Images ◽  
Som Neural Network ◽  
Brain Mr Images

Whole Tumor Segmentation from Brain MR images using Multi-view 2D Convolutional Neural Network

2021 ◽  
Author(s):  
Ritu Lahoti ◽  
Sunil Kumar Vengalil ◽  
Punith B Venkategowda ◽  
Neelam Sinha ◽  
Vinod Veera Reddy
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Tumor Segmentation ◽  
Mr Images ◽  
Brain Mr Images

An Automatical Segmentation Method for Brain MR Images

2016 ◽  
Author(s):  
Yunjie Chen ◽  
Zhenkai Wang ◽  
Byeungwoo Jeon ◽  
Jin Wang ◽  
Jeong-Uk Kim
Keyword(s):  
Segmentation Method ◽  
Mr Images ◽  
Brain Mr Images

Intelligent Diagnosis Method of MRI Brain Image Using Parallel Self-Organizing Feature Maps Neural Network

2021 ◽  
Vol 11 (2) ◽  
pp. 487-496
Author(s):  
Li Liu ◽  
Chi Hua ◽  
Zixuan Cheng ◽  
Yunfeng Ji
Keyword(s):  
Neural Network ◽  
Image Segmentation ◽  
Medical Imaging ◽  
Automatic Segmentation ◽  
Learning Ability ◽  
Mr Images ◽  
Feature Maps ◽  
Intelligent Diagnosis ◽  
Brain Mr Images ◽  
Self Organizing

Advances in medical imaging skills have promoted the influence of medical imaging in neuroscience. Having advanced medical imaging technology is essential for the medical industry. Magnetic resonance imaging (MRI) plays a central role in medical imaging. It plays a key role in the treatment of various human diseases. Doctors analyze brain size, shape, and location in brain MR images to assess brain disease and develop a medical plan. The manual division of brain tissue by experts is heavy and subjective. Therefore, the study of automatic segmentation of brain MR images has practical significance. Because the characteristics of brain MRI images are low contrast and high noise, which seriously affects the accuracy of image segmentation, the current image segmentation methods have some limitations in this application. In this paper, multiple self-organizing feature maps neural network (SOM-NN) are utilized to construct a parallel self-organizing feature maps neural network (PSOM-NN), which converts the segmentation problem of brain images into the classification problem of PSOMNN. The experiments show that SOM has strong self-learning ability in learning and training, and the parallel ability of PSOM-NN model greatly reduces the segmentation time, improves the real-time performance of the model, and helps to realize fully automatic or semi-automatic segmentation of the lesion area. PSOM can promote the improvement of segmentation accuracy and facilitate intelligent diagnosis.

scholarly journals Early Tumor Diagnosis in Brain MR Images via Deep Convolutional Neural Network Model

2021 ◽  
Vol 68 (2) ◽  
pp. 2413-2429
Author(s):  
Tapan Kumar Das ◽  
Pradeep Kumar Roy ◽  
Mohy Uddin ◽  
Kathiravan Srinivasan ◽  
Chuan-Yu Chang ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Deep Convolutional Neural Network ◽  
Tumor Diagnosis ◽  
Mr Images ◽  
Brain Mr Images ◽  
Early Tumor

Neural Network Mapping for Nonlinear Stereotactic Normalization of Brain MR Images

1993 ◽  
Vol 17 (3) ◽  
pp. 455-460 ◽  
Author(s):  
Yukio Kosugi ◽  
Mikiya Sase ◽  
Hiroshi Kuwatani ◽  
Naoyuki Kinoshita ◽  
Toshimitsu Momose ◽  
...  
Keyword(s):  
Neural Network ◽  
Mr Images ◽  
Brain Mr Images ◽  
Network Mapping

scholarly journals 3D Quantum-inspired Self-supervised Tensor Network for Volumetric Segmentation of Brain MR Images

2020 ◽  
Author(s):  
Debanjan Konar ◽  
Siddhartha Bhattacharyya ◽  
Tapan Kumar Gandhi ◽  
Bijaya Ketan Panigrahi ◽  
Richard Jiang
Keyword(s):  
Neural Network ◽  
Image Data ◽  
Tensor Decomposition ◽  
Semantic Segmentation ◽  
Mr Images ◽  
Convolutional Network ◽  
Data Set ◽  
Network Operations ◽  
Brain Mr Images ◽  
Volumetric Segmentation

<div>This paper introduces a novel shallow self-supervised tensor neural network for volumetric segmentation of brain MR images obviating training or supervision. The proposed network is a 3D version of the Quantum-Inspired Self Supervised Neural Network (QIS-Net) architecture and is referred to as 3D Quantum-inspired Self-supervised Tensor Neural Network (3D-QNet). The underlying architecture of 3D-QNet is composed of a trinity of volumetric layers viz. input, intermediate and output layers inter-connected using a 26-connected third-order neighborhood-based topology for voxel-wise processing of 3D MR image data suitable for semantic segmentation. Each of the volumetric layers contains quantum neurons designated by qubits or quantum bits. The incorporation</div><div>of tensor decomposition in quantum formalism leads to faster convergence of the network operations to preclude the inherent slow convergence problems faced by the self-supervised networks. The segmented volumes are obtained once the network converges. The suggested 3D-QNet is tailored and tested on the BRATS 2019 data set extensively in the experiments carried out. 3D-QNet has achieved promising dice similarity while compared with the intensively supervised convolutional network-based models 3D-UNet, Vox-ResNet, DRINet, and 3D-ESPNet, thus facilitating annotation free semantic segmentation using a self-supervised shallow network.</div>

scholarly journals Segmentation of Brain Tumors Using Three-Dimensional Convolutional Neural Network on MRI Images 3D MedImg-CNN

2021 ◽  
Vol 15 (4) ◽  
pp. 0-0
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
High Performance ◽  
Three Dimensional ◽  
Tumor Segmentation ◽  
Dice Similarity Coefficient ◽  
Segmentation Method ◽  
Mr Images ◽  
Brain Tumor Segmentation ◽  
Fully Automatic

We consider the problem of fully automatic brain tumor segmentation in MR images containing glioblastomas. We propose a three Dimensional Convolutional Neural Network (3D MedImg-CNN) approach which achieves high performance while being extremely efficient, a balance that existing methods have struggled to achieve. Our 3D MedImg-CNN is formed directly on the raw image modalities and thus learn a characteristic representation directly from the data. We propose a new cascaded architecture with two pathways that each model normal details in tumors. Fully exploiting the convolutional nature of our model also allows us to segment a complete cerebral image in one minute. The performance of the proposed 3D MedImg-CNN with CNN segmentation method is computed using dice similarity coefficient (DSC). In experiments on the 2013, 2015 and 2017 BraTS challenges datasets; we unveil that our approach is among the most powerful methods in the literature, while also being very effective.

scholarly journals 3D Quantum-inspired Self-supervised Tensor Network for Volumetric Segmentation of Brain MR Images

2020 ◽  
Author(s):  
Debanjan Konar ◽  
Siddhartha Bhattacharyya ◽  
Tapan Kumar Gandhi ◽  
Bijaya Ketan Panigrahi ◽  
Richard Jiang
Keyword(s):  
Neural Network ◽  
Image Data ◽  
Tensor Decomposition ◽  
Semantic Segmentation ◽  
Mr Images ◽  
Convolutional Network ◽  
Data Set ◽  
Network Operations ◽  
Brain Mr Images ◽  
Volumetric Segmentation

<div>This paper introduces a novel shallow self-supervised tensor neural network for volumetric segmentation of brain MR images obviating training or supervision. The proposed network is a 3D version of the Quantum-Inspired Self Supervised Neural Network (QIS-Net) architecture and is referred to as 3D Quantum-inspired Self-supervised Tensor Neural Network (3D-QNet). The underlying architecture of 3D-QNet is composed of a trinity of volumetric layers viz. input, intermediate and output layers inter-connected using a 26-connected third-order neighborhood-based topology for voxel-wise processing of 3D MR image data suitable for semantic segmentation. Each of the volumetric layers contains quantum neurons designated by qubits or quantum bits. The incorporation</div><div>of tensor decomposition in quantum formalism leads to faster convergence of the network operations to preclude the inherent slow convergence problems faced by the self-supervised networks. The segmented volumes are obtained once the network converges. The suggested 3D-QNet is tailored and tested on the BRATS 2019 data set extensively in the experiments carried out. 3D-QNet has achieved promising dice similarity while compared with the intensively supervised convolutional network-based models 3D-UNet, Vox-ResNet, DRINet, and 3D-ESPNet, thus facilitating annotation free semantic segmentation using a self-supervised shallow network.</div>

scholarly journals 3D Quantum-inspired Self-supervised Tensor Network for Volumetric Segmentation of Brain MR Images

2020 ◽  
Author(s):  
Debanjan Konar ◽  
Siddhartha Bhattacharyya ◽  
Tapan Kumar Gandhi ◽  
Bijaya Ketan Panigrahi
Keyword(s):  
Neural Network ◽  
Image Data ◽  
Tensor Decomposition ◽  
Semantic Segmentation ◽  
Mr Images ◽  
Convolutional Network ◽  
Data Set ◽  
Network Operations ◽  
Brain Mr Images ◽  
Volumetric Segmentation

<div>This paper introduces a novel shallow self-supervised tensor neural network for volumetric segmentation of brain MR images obviating training or supervision. The proposed network is a 3D version of the Quantum-Inspired Self Supervised Neural Network (QIS-Net) architecture and is referred to as 3D Quantum-inspired Self-supervised Tensor Neural Network (3D-QNet). The underlying architecture of 3D-QNet is composed of a trinity of volumetric layers viz. input, intermediate and output layers inter-connected using a 26-connected third-order neighborhood-based topology for voxel-wise processing of 3D MR image data suitable for semantic segmentation. Each of the volumetric layers contains quantum neurons designated by qubits or quantum bits. The incorporation</div><div>of tensor decomposition in quantum formalism leads to faster convergence of the network operations to preclude the inherent slow convergence problems faced by the self-supervised networks. The segmented volumes are obtained once the network converges. The suggested 3D-QNet is tailored and tested on the BRATS 2019 data set extensively in the experiments carried out. 3D-QNet has achieved promising dice similarity while compared with the intensively supervised convolutional network-based models 3D-UNet, Vox-ResNet, DRINet, and 3D-ESPNet, thus facilitating annotation free semantic segmentation using a self-supervised shallow network.</div>

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