scholarly journals Semantic Segmentation of 3D Medical Images with 3D Convolutional Neural Networks

2020 ◽  
Vol 23 (1) ◽  
Author(s):  
Alejandra Márquez Herrera ◽  
Alex J. Cuadros-Vargas ◽  
Helio Pedrini
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Loss Function ◽  
Medical Images ◽  
Three Dimensional ◽  
Ground Truth ◽  
Semantic Segmentation ◽  
Volumetric Medical Images ◽  
Magnetic Resonance Imaging Mri ◽  
3D Cnn

A neural network is a mathematical model that is able to perform a task automatically or semi-automatically after learning the human knowledge that we provided. Moreover, a Convolutional Neural Network (CNN) is a type of neural network that has shown to efficiently learn tasks related to the area of image analysis, such as image segmentation, whose main purpose is to find regions or separable objects within an image. A more specific type of segmentation, called semantic segmentation, guarantees that each region has a semantic meaning by giving it a label or class. Since CNNs can automate the task of image semantic segmentation, they have been very useful for the medical area, applying them to the segmentation of organs or abnormalities (tumors). This work aims to improve the task of binary semantic segmentation of volumetric medical images acquired by Magnetic Resonance Imaging (MRI) using a pre-existing Three-Dimensional Convolutional Neural Network (3D CNN) architecture. We propose a formulation of a loss function for training this 3D CNN, for improving pixel-wise segmentation results. This loss function is formulated based on the idea of adapting a similarity coefficient, used for measuring the spatial overlap between the prediction and ground truth, and then using it to train the network. As contribution, the developed approach achieved good performance in a context where the pixel classes are imbalanced. We show how the choice of the loss function for training can affect the nal quality of the segmentation. We validate our proposal over two medical image semantic segmentation datasets and show comparisons in performance between the proposed loss function and other pre-existing loss functions used for binary semantic segmentation.

scholarly journals SEEK: A Framework of Superpixel Learning with CNN Features for Unsupervised Segmentation

Electronics ◽  
2020 ◽  
Vol 9 (3) ◽  
pp. 383 ◽  
Author(s):  
Talha Ilyas ◽  
Abbas Khan ◽  
Muhammad Umraiz ◽  
Hyongsuk Kim
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Image Enhancement ◽  
Ground Truth ◽  
Semantic Segmentation ◽  
Segmentation Method ◽  
Unsupervised Segmentation ◽  
Segmentation Algorithms ◽  
Salient Features ◽  
The Given

Supervised semantic segmentation algorithms have been a hot area of exploration recently, but now the attention is being drawn towards completely unsupervised semantic segmentation. In an unsupervised framework, neither the targets nor the ground truth labels are provided to the network. That being said, the network is unaware about any class instance or object present in the given data sample. So, we propose a convolutional neural network (CNN) based architecture for unsupervised segmentation. We used the squeeze and excitation network, due to its peculiar ability to capture the features’ interdependencies, which increases the network’s sensitivity to more salient features. We iteratively enable our CNN architecture to learn the target generated by a graph-based segmentation method, while simultaneously preventing our network from falling into the pit of over-segmentation. Along with this CNN architecture, image enhancement and refinement techniques are exploited to improve the segmentation results. Our proposed algorithm produces improved segmented regions that meet the human level segmentation results. In addition, we evaluate our approach using different metrics to show the quantitative outperformance.

scholarly journals Adversarial Erasing method based on graph neural network

2021 ◽  
Vol 2083 (4) ◽  
pp. 042083
Author(s):  
Shuhan Liu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Loss Function ◽  
Deep Neural Networks ◽  
Ground Truth ◽  
Semantic Segmentation ◽  
Data Sets ◽  
Recent Developments ◽  
Weakly Supervised ◽  
Network Weight

Abstract Semantic segmentation is a traditional task that requires a large number of pixel-level ground truth label data sets, which is time-consuming and expensive. Recent developments in weakly-supervised settings have shown that reasonable performance can be obtained using only image-level labels. Classification is often used as an agent task to train deep neural networks and extract attention maps from them. The classification task only needs less supervision information to obtain the most discriminative part of the object. For this purpose, we propose a new end-to-end counter-wipe network. Compared with the baseline network, we propose a method to apply the graph neural network to obtain the first CAM. It is proposed to train the joint loss function to avoid the network weight sharing and cause the network to fall into a saddle point. Our experiments on the Pascal VOC2012 dataset show that 64.9% segmentation performance is obtained, which is an improvement of 2.1% compared to our baseline.

scholarly journals Identification and Diagnosis of Cerebral Stroke through Deep Convolutional Neural Network-Based Multimodal MRI Images

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Yanyan Pan ◽  
Huiping Zhang ◽  
Jinsuo Yang ◽  
Jing Guo ◽  
Zhiguo Yang ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Automatic Segmentation ◽  
Three Dimensional ◽  
Deep Convolutional Neural Network ◽  
Cerebral Stroke ◽  
Good Convergence ◽  
Segmentation Accuracy ◽  
Multimodal Mri ◽  
Magnetic Resonance Imaging Mri

This study aimed to explore the application value of multimodal magnetic resonance imaging (MRI) images based on the deep convolutional neural network (Conv.Net) in the diagnosis of strokes. Specifically, four automatic segmentation algorithms were proposed to segment multimodal MRI images of stroke patients. The segmentation effects were evaluated factoring into DICE, accuracy, sensitivity, and segmentation distance coefficient. It was found that although two-dimensional (2D) full convolutional neural network-based segmentation algorithm can locate and segment the lesion, its accuracy was low; the three-dimensional one exhibited higher accuracy, with various objective indicators improved, and the segmentation accuracy of the training set and the test set was 0.93 and 0.79, respectively, meeting the needs of automatic diagnosis. The asymmetric 3D residual U-Net network had good convergence and high segmentation accuracy, and the 3D deep residual network proposed on its basis had good segmentation coefficients, which can not only ensure segmentation accuracy but also avoid network degradation problems. In conclusion, the Conv.Net model can accurately segment the foci of patients with ischemic stroke and is suggested in clinic.

scholarly journals Spatiotemporal Convolutional Neural Network with Convolutional Block Attention Module for Micro-Expression Recognition

Information ◽  
2020 ◽  
Vol 11 (8) ◽  
pp. 380
Author(s):  
Boyu Chen ◽  
Zhihao Zhang ◽  
Nian Liu ◽  
Yang Tan ◽  
Xinyu Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Three Dimensional ◽  
Expression Recognition ◽  
Learning Framework ◽  
The Face ◽  
3D Cnn ◽  
Visual Attention Mechanism ◽  
Casme Ii ◽  
Micro Expression

A micro-expression is defined as an uncontrollable muscular movement shown on the face of humans when one is trying to conceal or repress his true emotions. Many researchers have applied the deep learning framework to micro-expression recognition in recent years. However, few have introduced the human visual attention mechanism to micro-expression recognition. In this study, we propose a three-dimensional (3D) spatiotemporal convolutional neural network with the convolutional block attention module (CBAM) for micro-expression recognition. First image sequences were input to a medium-sized convolutional neural network (CNN) to extract visual features. Afterwards, it learned to allocate the feature weights in an adaptive manner with the help of a convolutional block attention module. The method was testified in spontaneous micro-expression databases (Chinese Academy of Sciences Micro-expression II (CASME II), Spontaneous Micro-expression Database (SMIC)). The experimental results show that the 3D CNN with convolutional block attention module outperformed other algorithms in micro-expression recognition.

scholarly journals Filling the binary images of draped fabric with pix2pix convolutional neural network

2020 ◽  
Vol 15 ◽  
pp. 155892502092154
Author(s):  
Zhicai Yu ◽  
Yueqi Zhong ◽  
R Hugh Gong ◽  
Haoyang Xie
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Point Cloud ◽  
Three Dimensional ◽  
Ground Truth ◽  
Binary Image ◽  
Grayscale Image ◽  
Binary Images ◽  
The Relationship ◽  
Grayscale Images

To fill the binary image of draped fabric into a comparable grayscale image with detailed shade information, the three-dimensional point cloud of draped fabric was obtained with a self-built three-dimensional scanning device. The three-dimensional point cloud of drape fabric is encapsulated into a triangular mesh, and the binary and grayscale images of draped fabric were rendered in virtual environments separately. A pix2pix convolutional neural network with the binary image of draped fabric as input and the grayscale image of draped fabric as output was constructed and trained. The relationship between the binary image and the grayscale image was established. The results show that the trained pix2pix neural network can fill unknown binary top view images of draped fabric to grayscale images. The average pixel cosine similarity between filling results and ground truth could reach 0.97.

scholarly journals Development of a Convolutional Neural Network Based Skull Segmentation in MRI Using Standard Tesselation Language Models

2021 ◽  
Vol 11 (4) ◽  
pp. 310
Author(s):  
Rodrigo Dalvit Carvalho da Silva ◽  
Thomas Richard Jenkyn ◽  
Victor Alexander Carranza
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Language Models ◽  
Dice Similarity Coefficient ◽  
Imaging Analysis ◽  
Resonance Imaging ◽  
Mr Images ◽  
Magnetic Resonance Imaging Mri ◽  
3D Cnn ◽  
The Brain

Segmentation is crucial in medical imaging analysis to help extract regions of interest (ROI) from different imaging modalities. The aim of this study is to develop and train a 3D convolutional neural network (CNN) for skull segmentation in magnetic resonance imaging (MRI). 58 gold standard volumetric labels were created from computed tomography (CT) scans in standard tessellation language (STL) models. These STL models were converted into matrices and overlapped on the 58 corresponding MR images to create the MRI gold standards labels. The CNN was trained with these 58 MR images and a mean ± standard deviation (SD) Dice similarity coefficient (DSC) of 0.7300 ± 0.04 was achieved. A further investigation was carried out where the brain region was removed from the image with the help of a 3D CNN and manual corrections by using only MR images. This new dataset, without the brain, was presented to the previous CNN which reached a new mean ± SD DSC of 0.7826 ± 0.03. This paper aims to provide a framework for segmenting the skull using CNN and STL models, as the 3D CNN was able to segment the skull with a certain precision.

scholarly journals Three-Dimensional Convolutional Neural Network Model for Early Detection of Pine Wilt Disease Using UAV-Based Hyperspectral Images

2021 ◽  
Vol 13 (20) ◽  
pp. 4065
Author(s):  
Run Yu ◽  
Youqing Luo ◽  
Haonan Li ◽  
Liyuan Yang ◽  
Huaguo Huang ◽  
...  
Keyword(s):  
Neural Network ◽  
Early Detection ◽  
Convolutional Neural Network ◽  
Three Dimensional ◽  
Pine Wilt Disease ◽  
Wilt Disease ◽  
Hyperspectral Images ◽  
Pine Wilt ◽  
Pine Trees ◽  
3D Cnn

As one of the most devastating disasters to pine forests, pine wilt disease (PWD) has caused tremendous ecological and economic losses in China. An effective way to prevent large-scale PWD outbreaks is to detect and remove the damaged pine trees at the early stage of PWD infection. However, early infected pine trees do not show obvious changes in morphology or color in the visible wavelength range, making early detection of PWD tricky. Unmanned aerial vehicle (UAV)-based hyperspectral imagery (HI) has great potential for early detection of PWD. However, the commonly used methods, such as the two-dimensional convolutional neural network (2D-CNN), fail to simultaneously extract and fully utilize the spatial and spectral information, whereas the three-dimensional convolutional neural network (3D-CNN) is able to collect this information from raw hyperspectral data. In this paper, we applied the residual block to 3D-CNN and constructed a 3D-Res CNN model, the performance of which was then compared with that of 3D-CNN, 2D-CNN, and 2D-Res CNN in identifying PWD-infected pine trees from the hyperspectral images. The 3D-Res CNN model outperformed the other models, achieving an overall accuracy (OA) of 88.11% and an accuracy of 72.86% for detecting early infected pine trees (EIPs). Using only 20% of the training samples, the OA and EIP accuracy of 3D-Res CNN can still achieve 81.06% and 51.97%, which is superior to the state-of-the-art method in the early detection of PWD based on hyperspectral images. Collectively, 3D-Res CNN was more accurate and effective in early detection of PWD. In conclusion, 3D-Res CNN is proposed for early detection of PWD in this paper, making the prediction and control of PWD more accurate and effective. This model can also be applied to detect pine trees damaged by other diseases or insect pests in the forest.

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