MRU-NET: A U-Shaped Network for Retinal Vessel Segmentation

Fundus blood vessel image segmentation plays an important role in the diagnosis and treatment of diseases and is the basis of computer-aided diagnosis. Feature information from the retinal blood vessel image is relatively complicated, and the existing algorithms are sometimes difficult to perform effective segmentation with. Aiming at the problems of low accuracy and low sensitivity of the existing segmentation methods, an improved U-shaped neural network (MRU-NET) segmentation method for retinal vessels was proposed. Firstly, the image enhancement algorithm and random segmentation method are used to solve the problems of low contrast and insufficient image data of the original image. Moreover, smaller image blocks after random segmentation are helpful to reduce the complexity of the U-shaped neural network model; secondly, the residual learning is introduced into the encoder and decoder to improve the efficiency of feature use and to reduce information loss, and a feature fusion module is introduced between the encoder and decoder to extract image features with different granularities; and finally, a feature balancing module is added to the skip connections to resolve the semantic gap between low-dimensional features in the encoder and high-dimensional features in decoder. Experimental results show that our method has better accuracy and sensitivity on the DRIVE and STARE datasets (accuracy (ACC) = 0.9611, sensitivity (SE) = 0.8613; STARE: ACC = 0.9662, SE = 0.7887) than some of the state-of-the-art methods.

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Retinal Vessel Segmentation by Deep Residual Learning with Wide Activation

Computational Intelligence and Neuroscience ◽

10.1155/2020/8822407 ◽

2020 ◽

Vol 2020 ◽

pp. 1-11

Author(s):

Yuliang Ma ◽

Xue Li ◽

Xiaopeng Duan ◽

Yun Peng ◽

Yingchun Zhang

Keyword(s):

Blood Vessel ◽

Blood Vessels ◽

Area Under The Curve ◽

Retinal Vessel ◽

Vessel Segmentation ◽

Superior Performance ◽

Retinal Blood Vessel ◽

Low Contrast ◽

Blood Vessel Segmentation ◽

Small Vessels

Purpose. Retinal blood vessel image segmentation is an important step in ophthalmological analysis. However, it is difficult to segment small vessels accurately because of low contrast and complex feature information of blood vessels. The objective of this study is to develop an improved retinal blood vessel segmentation structure (WA-Net) to overcome these challenges. Methods. This paper mainly focuses on the width of deep learning. The channels of the ResNet block were broadened to propagate more low-level features, and the identity mapping pathway was slimmed to maintain parameter complexity. A residual atrous spatial pyramid module was used to capture the retinal vessels at various scales. We applied weight normalization to eliminate the impacts of the mini-batch and improve segmentation accuracy. The experiments were performed on the DRIVE and STARE datasets. To show the generalizability of WA-Net, we performed cross-training between datasets. Results. The global accuracy and specificity within datasets were 95.66% and 96.45% and 98.13% and 98.71%, respectively. The accuracy and area under the curve of the interdataset diverged only by 1%∼2% compared with the performance of the corresponding intradataset. Conclusion. All the results show that WA-Net extracts more detailed blood vessels and shows superior performance on retinal blood vessel segmentation tasks.

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A Method of Fast Segmentation for Banana Stalk Exploited Lightweight Multi-Feature Fusion Deep Neural Network

Machines ◽

10.3390/machines9030066 ◽

2021 ◽

Vol 9 (3) ◽

pp. 66

Author(s):

Tianci Chen ◽

Rihong Zhang ◽

Lixue Zhu ◽

Shiang Zhang ◽

Xiaomin Li

Keyword(s):

Neural Network ◽

Execution Time ◽

Deep Neural Network ◽

Feature Fusion ◽

Frame Rate ◽

Light Conditions ◽

Segmentation Method ◽

Segmentation Accuracy ◽

Automatic Picking ◽

Changing Light

In an orchard environment with a complex background and changing light conditions, the banana stalk, fruit, branches, and leaves are very similar in color. The fast and accurate detection and segmentation of a banana stalk are crucial to realize the automatic picking using a banana picking robot. In this paper, a banana stalk segmentation method based on a lightweight multi-feature fusion deep neural network (MFN) is proposed. The proposed network is mainly composed of encoding and decoding networks, in which the sandglass bottleneck design is adopted to alleviate the information a loss in high dimension. In the decoding network, a different sized dilated convolution kernel is used for convolution operation to make the extracted banana stalk features denser. The proposed network is verified by experiments. In the experiments, the detection precision, segmentation accuracy, number of parameters, operation efficiency, and average execution time are used as evaluation metrics, and the proposed network is compared with Resnet_Segnet, Mobilenet_Segnet, and a few other networks. The experimental results show that compared to other networks, the number of network parameters of the proposed network is significantly reduced, the running frame rate is improved, and the average execution time is shortened.

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The International Radiomics Platform – An Initiative of the German and Austrian Radiological Societies

RöFo - Fortschritte auf dem Gebiet der Röntgenstrahlen und der bildgebenden Verfahren ◽

10.1055/a-1244-2775 ◽

2020 ◽

Author(s):

Daniel Overhoff ◽

Peter Kohlmann ◽

Alex Frydrychowicz ◽

Sergios Gatidis ◽

Christian Loewe ◽

...

Keyword(s):

Neural Network ◽

Artificial Intelligence ◽

Image Data ◽

Quality Parameters ◽

Image Features ◽

Performance Criteria ◽

Data Sets ◽

Proof Of Concept ◽

Concept Study ◽

Artificial Intelligence Methods

Purpose The DRG-ÖRG IRP (Deutsche Röntgengesellschaft-Österreichische Röntgengesellschaft international radiomics platform) represents a web-/cloud-based radiomics platform based on a public-private partnership. It offers the possibility of data sharing, annotation, validation and certification in the field of artificial intelligence, radiomics analysis, and integrated diagnostics. In a first proof-of-concept study, automated myocardial segmentation and automated myocardial late gadolinum enhancement (LGE) detection using radiomic image features will be evaluated for myocarditis data sets. Materials and Methods The DRG-ÖRP IRP can be used to create quality-assured, structured image data in combination with clinical data and subsequent integrated data analysis and is characterized by the following performance criteria: Possibility of using multicentric networked data, automatically calculated quality parameters, processing of annotation tasks, contour recognition using conventional and artificial intelligence methods and the possibility of targeted integration of algorithms. In a first study, a neural network pre-trained using cardiac CINE data sets was evaluated for segmentation of PSIR data sets. In a second step, radiomic features were applied for segmental detection of LGE of the same data sets, which were provided multicenter via the IRP. Results First results show the advantages (data transparency, reliability, broad involvement of all members, continuous evolution as well as validation and certification) of this platform-based approach. In the proof-of-concept study, the neural network demonstrated a Dice coefficient of 0.813 compared to the expert's segmentation of the myocardium. In the segment-based myocardial LGE detection, the AUC was 0.73 and 0.79 after exclusion of segments with uncertain annotation.The evaluation and provision of the data takes place at the IRP, taking into account the FAT (fairness, accountability, transparency) and FAIR (findable, accessible, interoperable, reusable) criteria. Conclusion It could be shown that the DRG-ÖRP IRP can be used as a crystallization point for the generation of further individual and joint projects. The execution of quantitative analyses with artificial intelligence methods is greatly facilitated by the platform approach of the DRG-ÖRP IRP, since pre-trained neural networks can be integrated and scientific groups can be networked.In a first proof-of-concept study on automated segmentation of the myocardium and automated myocardial LGE detection, these advantages were successfully applied.Our study shows that with the DRG-ÖRP IRP, strategic goals can be implemented in an interdisciplinary way, that concrete proof-of-concept examples can be demonstrated, and that a large number of individual and joint projects can be realized in a participatory way involving all groups. Key Points: Citation Format

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An Efficient and Lightweight Convolutional Neural Network for Remote Sensing Image Scene Classification

Sensors ◽

10.3390/s20071999 ◽

2020 ◽

Vol 20 (7) ◽

pp. 1999 ◽

Cited By ~ 6

Author(s):

Donghang Yu ◽

Qing Xu ◽

Haitao Guo ◽

Chuan Zhao ◽

Yuzhun Lin ◽

...

Keyword(s):

Neural Network ◽

Remote Sensing ◽

Convolutional Neural Network ◽

Visual Recognition ◽

Feature Fusion ◽

Remote Sensing Image ◽

Classification Performance ◽

Image Features ◽

Training Dataset ◽

Scene Classification

Classifying remote sensing images is vital for interpreting image content. Presently, remote sensing image scene classification methods using convolutional neural networks have drawbacks, including excessive parameters and heavy calculation costs. More efficient and lightweight CNNs have fewer parameters and calculations, but their classification performance is generally weaker. We propose a more efficient and lightweight convolutional neural network method to improve classification accuracy with a small training dataset. Inspired by fine-grained visual recognition, this study introduces a bilinear convolutional neural network model for scene classification. First, the lightweight convolutional neural network, MobileNetv2, is used to extract deep and abstract image features. Each feature is then transformed into two features with two different convolutional layers. The transformed features are subjected to Hadamard product operation to obtain an enhanced bilinear feature. Finally, the bilinear feature after pooling and normalization is used for classification. Experiments are performed on three widely used datasets: UC Merced, AID, and NWPU-RESISC45. Compared with other state-of-art methods, the proposed method has fewer parameters and calculations, while achieving higher accuracy. By including feature fusion with bilinear pooling, performance and accuracy for remote scene classification can greatly improve. This could be applied to any remote sensing image classification task.

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BLNN: Multiscale Feature Fusion-Based Bilinear Fine-Grained Convolutional Neural Network for Image Classification of Wood Knot Defects

Journal of Sensors ◽

10.1155/2021/8109496 ◽

2021 ◽

Vol 2021 ◽

pp. 1-18

Author(s):

Mingyu Gao ◽

Fei Wang ◽

Peng Song ◽

Junyan Liu ◽

DaWei Qi

Keyword(s):

Neural Network ◽

Deep Learning ◽

Defect Detection ◽

Feature Fusion ◽

Image Features ◽

Optical Image ◽

Training Time ◽

Fine Grained ◽

Detection Model ◽

Defect Recognition

Wood defects are quickly identified from an optical image based on deep learning methodology, which effectively improves the wood utilization. The traditional neural network technique is unemployed for the wood defect detection of optical image used, which results from a long training time, low recognition accuracy, and nonautomatic extraction of defect image features. In this paper, a wood knot defect detection model (so-called BLNN) combined deep learning is reported. Two subnetworks composed of convolutional neural networks are trained by Pytorch. By using the feature extraction capabilities of the two subnetworks and combining the bilinear join operation, the fine-grained features of the image are obtained. The experimental results show that the accuracy has reached up 99.20%, and the training time is obviously reduced with the speed of defect detection about 0.0795 s/image. It indicates that BLNN has the ability to improve the accuracy of defect recognition and has a potential application in the detection of wood knot defects.

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CT Image Segmentation of Liver Tumor with Deep Convolutional Neural Network

Journal of Medical Imaging and Health Informatics ◽

10.1166/jmihi.2021.3295 ◽

2021 ◽

Vol 11 (2) ◽

pp. 337-344

Author(s):

Yao Zeng ◽

Huanhuan Dai

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Automatic Segmentation ◽

Abdominal Cavity ◽

Mutual Exclusion ◽

Image Data ◽

Deep Convolutional Neural Network ◽

Training Data ◽

Segmentation Method ◽

Segmentation Boundary

The liver is the largest substantial organ in the abdominal cavity of the human body. Its structure is complex, the incidence of vascular abundance is high, and it has been seriously ribbed, human health and life. In this study, an automatic segmentation method based on deep convolutional neural network is proposed. Image data blocks of different sizes are extracted as training data and different network structures are designed, and features are automatically learned to obtain a segmentation structure of the tumor. Secondly, in order to further refine the segmentation boundary, we establish a multi-region segmentation model with region mutual exclusion constraints. The model combines the image grayscale, gradient and prior probability information, and overcomes the problem that the boundary point attribution area caused by boundary blur and regional adhesion is difficult to determine. Finally, the model is solved quickly using the time-invisible multi-phase level set. Compared with the traditional multi-organ segmentation method, this method does not require registration or model initialization. The experimental results show that the model can segment the liver, kidney and spleen quickly and effectively, and the segmentation accuracy reaches the advanced level of current methods.

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Bi-SANet—Bilateral Network with Scale Attention for Retinal Vessel Segmentation

Symmetry ◽

10.3390/sym13101820 ◽

2021 ◽

Vol 13 (10) ◽

pp. 1820

Author(s):

Yun Jiang ◽

Huixia Yao ◽

Zeqi Ma ◽

Jingyao Zhang

Keyword(s):

Spatial Information ◽

Feature Fusion ◽

Receptive Fields ◽

Retinal Vessel ◽

Vessel Segmentation ◽

Low Contrast ◽

Noise Interference ◽

Multi Scale ◽

Retinal Vessel Segmentation ◽

Residual Block

The segmentation of retinal vessels is critical for the diagnosis of some fundus diseases. Retinal vessel segmentation requires abundant spatial information and receptive fields with different sizes while existing methods usually sacrifice spatial resolution to achieve real-time reasoning speed, resulting in inadequate vessel segmentation of low-contrast regions and weak anti-noise interference ability. The asymmetry of capillaries in fundus images also increases the difficulty of segmentation. In this paper, we proposed a two-branch network based on multi-scale attention to alleviate the above problem. First, a coarse network with multi-scale U-Net as the backbone is designed to capture more semantic information and to generate high-resolution features. A multi-scale attention module is used to obtain enough receptive fields. The other branch is a fine network, which uses the residual block of a small convolution kernel to make up for the deficiency of spatial information. Finally, we use the feature fusion module to aggregate the information of the coarse and fine networks. The experiments were performed on the DRIVE, CHASE, and STARE datasets. Respectively, the accuracy reached 96.93%, 97.58%, and 97.70%. The specificity reached 97.72%, 98.52%, and 98.94%. The F-measure reached 83.82%, 81.39%, and 84.36%. Experimental results show that compared with some state-of-art methods such as Sine-Net, SA-Net, our proposed method has better performance on three datasets.

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A deep neural network-based vehicle re-identification method for bridge load monitoring

Advances in Structural Engineering ◽

10.1177/13694332211033956 ◽

2021 ◽

pp. 136943322110339

Author(s):

Yufeng Zhang ◽

Junxin Xie ◽

Jiayi Peng ◽

Hui Li ◽

Yong Huang

Keyword(s):

Neural Network ◽

Feature Detection ◽

Image Data ◽

Image Features ◽

Superior Performance ◽

Multiple Sources ◽

Identification Method ◽

Load Monitoring ◽

Bridge Structures ◽

Point Feature

The accurate tracking of vehicle loads is essential for the condition assessment of bridge structures. In recent years, a computer vision method that is based on multiple sources of data from monitoring cameras and weight-in-motion (WIM) systems has become a promising strategy in bridge vehicle load identification for structural health monitoring (SHM) and has attracted increasing attention. The implementation of vehicle re-identification, namely, the identification of the same vehicle from images that were captured at different locations or time instants, is the key topic of this study. In this study, a vehicle re-identification method that is based on HardNet, a deep convolutional neural network (CNN) specialized in picking up local image features, is proposed. First, we obtain the vehicle point feature positions in the image through feature detection. Then, the HardNet is employed to encode the point feature image patches into deep learning feature descriptors. Re-identification of the target vehicle is achieved by matching the encoded descriptors between two images, which are robust toward scaling, rotation, and other types of noises. A comparison study of the proposed method with three published vehicle re-identification methods is performed using vehicle image data from a real bridge, and the superior performance of our proposed method is demonstrated.

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A Feature Fusion Method with Guided Training for Classification Tasks

Computational Intelligence and Neuroscience ◽

10.1155/2021/6647220 ◽

2021 ◽

Vol 2021 ◽

pp. 1-11

Author(s):

Taohong Zhang ◽

Suli Fan ◽

Junnan Hu ◽

Xuxu Guo ◽

Qianqian Li ◽

...

Keyword(s):

Feature Fusion ◽

Image Data ◽

Numerical Data ◽

Image Features ◽

Structured Data ◽

Fusion Method ◽

Training Images ◽

Proposed Model ◽

Classification Tasks ◽

And Training

In this paper, a feature fusion method with guiding training (FGT-Net) is constructed to fuse image data and numerical data for some specific recognition tasks which cannot be classified accurately only according to images. The proposed structure is divided into the shared weight network part, the feature fused layer part, and the classification layer part. First, the guided training method is proposed to optimize the training process, the representative images and training images are input into the shared weight network to learn the ability that extracts the image features better, and then the image features and numerical features are fused together in the feature fused layer to input into the classification layer for the classification task. Experiments are carried out to verify the effectiveness of the proposed model. Loss is calculated by the output of both the shared weight network and classification layer. The results of experiments show that the proposed FGT-Net achieves the accuracy of 87.8%, which is 15% higher than the CNN model of ShuffleNetv2 (which can process image data only) and 9.8% higher than the DNN method (which processes structured data only).

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Analysis of Tai Chi Ideological and Political Course in University Based on Big Data and Graph Neural Networks

Scientific Programming ◽

10.1155/2021/9914908 ◽

2021 ◽

Vol 2021 ◽

pp. 1-9

Author(s):

Chun yan Li ◽

Lu Zheng

Keyword(s):

Neural Network ◽

Big Data ◽

Tai Chi ◽

Image Features ◽

Basic Module ◽

Semantic Categories ◽

Interactive Classroom ◽

Spatio Temporal ◽

Low Dimensional

Tai Chi martial arts education is one of the components of school education. Its educational value is not only to require students to master basic Tai movement technical skills and improve their physical fitness but also to bring students’ ideological progress and cultivate students to respect teachers and lectures. Excellent moral qualities such as politeness, keeping promises, observing the rules, and acting bravely, as well as the cultivation of the spirit of unity and cooperation, and the quality of will also have a certain meaning. However, the scientific Tai Chi ideological and political courses and the construction of Wude education interactive classrooms lack relevant research. Therefore, this article builds a Tai Chi ideological and political interactive classroom system based on big data technology and graph neural network. First, the spatio-temporal graph convolutional neural network is used to reason about the relationship between Tai Chi action categories and strengthen the low-dimensional features of semantic categories and their co-occurrence expressions used for semantic enhancement of current image features. In addition, in order to ensure the efficiency of the Tai Chi scene analysis network, an efficient dual feature extraction basic module is proposed to construct the backbone network, reducing the number of parameters of the entire network and the computational complexity. Experiments show that this method can obtain approximate results, while reducing the amount of floating-point operations by 42.5% and the amount of parameters by 50.2% compared with the work of the same period, and achieves a better balance of efficiency and performance. Secondly, based on the big data of historical Tai Chi classrooms, this article constructs an interactive classroom system that can effectively improve the quality of Tai Chi ideological and political courses.

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