scholarly journals Automatic tissue image segmentation based on image processing and deep learning

2018 ◽  
Author(s):  
Zhenglun Kong ◽  
Ting Li ◽  
Junyi Luo ◽  
Shengpu Xu
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Deep Learning

scholarly journals Automatic Tissue Image Segmentation Based on Image Processing and Deep Learning

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Zhenglun Kong ◽  
Ting Li ◽  
Junyi Luo ◽  
Shengpu Xu
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Deep Learning ◽  
White Matter ◽  
Multimodality Imaging ◽  
Cerebral Atrophy ◽  
Light Transport ◽  
Structural Description ◽  
Semiautomatic Segmentation ◽  
Quantitative Visualization

Image segmentation plays an important role in multimodality imaging, especially in fusion structural images offered by CT, MRI with functional images collected by optical technologies, or other novel imaging technologies. In addition, image segmentation also provides detailed structural description for quantitative visualization of treating light distribution in the human body when incorporated with 3D light transport simulation methods. Here, we first use some preprocessing methods such as wavelet denoising to extract the accurate contours of different tissues such as skull, cerebrospinal fluid (CSF), grey matter (GM), and white matter (WM) on 5 MRI head image datasets. We then realize automatic image segmentation with deep learning by using convolutional neural network. We also introduce parallel computing. Such approaches greatly reduced the processing time compared to manual and semiautomatic segmentation and are of great importance in improving the speed and accuracy as more and more samples are being learned. The segmented data of grey and white matter are counted by computer in volume, which indicates the potential of this segmentation technology in diagnosing cerebral atrophy quantitatively. We demonstrate the great potential of such image processing and deep learning-combined automatic tissue image segmentation in neurology medicine.

scholarly journals Optic Disc Segmentation Using Attention-Based U-Net and the Improved Cross-Entropy Convolutional Neural Network

Entropy ◽  
2020 ◽  
Vol 22 (8) ◽  
pp. 844
Author(s):  
Baixin Jin ◽  
Pingping Liu ◽  
Peng Wang ◽  
Lida Shi ◽  
Jing Zhao
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Deep Learning ◽  
Optic Disc ◽  
Medical Image ◽  
Medical Image Analysis ◽  
Medical Image Segmentation ◽  
Attention Mechanism ◽  
Cross Entropy ◽  
Segmentation Task

Medical image segmentation is an important part of medical image analysis. With the rapid development of convolutional neural networks in image processing, deep learning methods have achieved great success in the field of medical image processing. Deep learning is also used in the field of auxiliary diagnosis of glaucoma, and the effective segmentation of the optic disc area plays an important assistant role in the diagnosis of doctors in the clinical diagnosis of glaucoma. Previously, many U-Net-based optic disc segmentation methods have been proposed. However, the channel dependence of different levels of features is ignored. The performance of fundus image segmentation in small areas is not satisfactory. In this paper, we propose a new aggregation channel attention network to make full use of the influence of context information on semantic segmentation. Different from the existing attention mechanism, we exploit channel dependencies and integrate information of different scales into the attention mechanism. At the same time, we improved the basic classification framework based on cross entropy, combined the dice coefficient and cross entropy, and balanced the contribution of dice coefficients and cross entropy loss to the segmentation task, which enhanced the performance of the network in small area segmentation. The network retains more image features, restores the significant features more accurately, and further improves the segmentation performance of medical images. We apply it to the fundus optic disc segmentation task. We demonstrate the segmentation performance of the model on the Messidor dataset and the RIM-ONE dataset, and evaluate the proposed architecture. Experimental results show that our network architecture improves the prediction performance of the base architectures under different datasets while maintaining the computational efficiency. The results render that the proposed technologies improve the segmentation with 0.0469 overlapping error on Messidor.

scholarly journals A Review of Deep-Learning-Based Medical Image Segmentation Methods

2021 ◽  
Vol 13 (3) ◽  
pp. 1224
Author(s):  
Xiangbin Liu ◽  
Liping Song ◽  
Shuai Liu ◽  
Yudong Zhang
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Deep Learning ◽  
Medical Image ◽  
Rapid Development ◽  
Medical Image Segmentation ◽  
Deep Convolutional Neural Networks ◽  
Biomedical Image Processing ◽  
Data Set ◽  
Segmentation Methods

As an emerging biomedical image processing technology, medical image segmentation has made great contributions to sustainable medical care. Now it has become an important research direction in the field of computer vision. With the rapid development of deep learning, medical image processing based on deep convolutional neural networks has become a research hotspot. This paper focuses on the research of medical image segmentation based on deep learning. First, the basic ideas and characteristics of medical image segmentation based on deep learning are introduced. By explaining its research status and summarizing the three main methods of medical image segmentation and their own limitations, the future development direction is expanded. Based on the discussion of different pathological tissues and organs, the specificity between them and their classic segmentation algorithms are summarized. Despite the great achievements of medical image segmentation in recent years, medical image segmentation based on deep learning has still encountered difficulties in research. For example, the segmentation accuracy is not high, the number of medical images in the data set is small and the resolution is low. The inaccurate segmentation results are unable to meet the actual clinical requirements. Aiming at the above problems, a comprehensive review of current medical image segmentation methods based on deep learning is provided to help researchers solve existing problems.

scholarly journals Spine Medical Image Segmentation Based on Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
Qingfeng Zhang ◽  
Yun Du ◽  
Zhiqiang Wei ◽  
Hengping Liu ◽  
Xiaoxia Yang ◽  
...  
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Deep Learning ◽  
Medical Image ◽  
Processing Time ◽  
Learning Algorithm ◽  
Medical Image Segmentation ◽  
Clinical Value ◽  
Convolutional Network ◽  
Spinal Mri

The aim was to further explore the clinical value of deep learning algorithm in the field of spinal medical image segmentation, and this study designed an improved U-shaped network (BN-U-Net) algorithm and applied it to the spinal MRI medical image segmentation of 22 research objects. The application value of this algorithm in MRI image processing was comprehensively evaluated by accuracy (Acc), sensitivity (Sen), specificity (Spe), and area under curve (AUC). The results show that the image processing time of fully convolutional network (FCN) algorithm and U-Net algorithm is greater than 6 min, while the processing time of BN-U-Net algorithm is only 5–10 s, and the processing time is significantly shortened ( P < 0.05 ). The Acc, Sen, and Spe results of BN-U-Net segmentation algorithm were 94.54 ± 3.56%, 88.76 ± 2.67%, and 86.27 ± 6.23%, respectively, which were significantly improved compared with FCN algorithm and U-Net algorithm ( P < 0.05 ). In summary, the improved U-Net network algorithm used in this study significantly improves the quality of spinal MRI images by automatic segmentation of MRI images, which is worthy of further promotion in the field of spinal medical image segmentation.

scholarly journals Mulberry Leaf Disease Detection using Deep Learning

2019 ◽  
Vol 9 (1) ◽  
pp. 3366-3371 ◽  
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Deep Learning ◽  
Plant Pathogens ◽  
Early Stage ◽  
Disease Diagnosis ◽  
Minimal Cost ◽  
Mulberry Leaf ◽  
Diagnosis And Classification ◽  
Mulberry Plant

Disease diagnosis and classification in a mulberry plant using deep learning is an interesting technique which can be useful for farmers and researchers to identify and classify diseases. It helps to manage plant pathogens within fields effectively and automatically at a minimal cost. Major mulberry diseases usually express their symptoms on leaf area at the early stage of infection. Infections can be analysed and classified by processing the image using a computer or machine using different algorithms to interpret the information. This paper gives us a brief knowledge of mulberry leaf diseases which is used for automatic detection of disease. It presents in detail that the algorithm and techniques which are involved in classification based on different criteria for image segmentation. Our goal is to develop a more suitable deep algorithm for our task. These convolutional layers are mostly used for image processing. The system identifies and classify mulberry leaf diseases effectively with complex scenarios from the affected areas using CNN.

scholarly journals Concrete Crack Detection Algorithm Based on Deep Residual Neural Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Xiuying Meng
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Deep Learning ◽  
Crack Detection ◽  
Learning Algorithm ◽  
Detection Algorithm ◽  
Concrete Pavement ◽  
Computer Hardware ◽  
Detection Accuracy ◽  
Generalization Ability

Crack is the early expression form of the concrete pavement disease. Early discovery and treatment of it can play an important role in the maintenance of the pavement. With ongoing advancements in computer hardware technology, continual optimization of deep learning algorithms, as compared to standard digital image processing algorithms, utilizing automation of crack detection technology has a deep learning algorithm that is more exact. As a result of the benefits of greater robustness, the study of concrete pavement crack picture has become popular. In view of the poor effect and weak generalization ability of traditional image processing technology on image segmentation of concrete cracks, this paper studies the image segmentation algorithm of concrete cracks based on convolutional neural network and designs an end-to-end segmentation model based on ResNet101. It integrates more low-level features, which make the fracture segmentation results more refined and closer to the practical application scenarios. Compared with other methods, the algorithm in this paper has achieved higher detection accuracy and generalization ability.

scholarly journals Plant Leaf Segmentation and Phenotypic Analysis Based on Fully Convolutional Neural Network

2021 ◽  
Vol 37 (5) ◽  
pp. 929-940
Author(s):  
Liying Cao ◽  
Hongda Li ◽  
Helong Yu ◽  
Guifen Chen ◽  
Heshu Wang
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Image Segmentation ◽  
Deep Learning ◽  
High Throughput ◽  
Loss Function ◽  
Small Sample ◽  
List Type ◽  
Plant Leaf ◽  
Segmentation Accuracy

HighlightsModify the U-Net segmentation network to reduce the loss of segmentation accuracy.Reducing the number of layers U-net network, modifying the loss function, and the increase in the output layer dropout.It can be well extracted after splitting blade morphological model and color feature.Abstract. From the perspective of computer vision, the shortcut to extract phenotypic information from a single crop in the field is image segmentation. Plant segmentation is affected by the background environment and illumination. Using deep learning technology to combine depth maps with multi-view images can achieve high-throughput image processing. This article proposes an improved U-Net segmentation network, based on small sample data enhancement, and reconstructs the U-Net model by optimizing the model framework, activation function and loss function. It is used to realize automatic segmentation of plant leaf images and extract relevant feature parameters. Experimental results show that the improved model can provide reliable segmentation results under different leaf sizes, different lighting conditions, different backgrounds, and different plant leaves. The pixel-by-pixel segmentation accuracy reaches 0.94. Compared with traditional methods, this network achieves robust and high-throughput image segmentation. This method is expected to provide key technical support and practical tools for top-view image processing, Unmanned Aerial Vehicle phenotype extraction, and phenotype field platforms. Keywords: Deep learning, Full convolution neural network, Image segmentation, Phenotype analysis, U-Net.

Deep Learning-Based Image Processing for Whitecaps on the Ocean Surface

2020 ◽  
Vol 76 (2) ◽  
pp. I_163-I_168
Author(s):  
Yukun WANG ◽  
Yuji SUGIHARA ◽  
Xianting ZHAO ◽  
Haruki NAKASHIMA ◽  
Osama ELJAMAL
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Ocean Surface
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