Medical Image Segmentation based on U-Net: A Review

2020 ◽  
Vol 64 (2) ◽  
pp. 20508-1-20508-12 ◽  
Author(s):  
Getao Du ◽  
Xu Cao ◽  
Jimin Liang ◽  
Xueli Chen ◽  
Yonghua Zhan
Keyword(s):  
Neural Network ◽  
Image Analysis ◽  
Image Segmentation ◽  
Deep Learning ◽  
Medical Imaging ◽  
Medical Image ◽  
Medical Images ◽  
Medical Image Analysis ◽  
Medical Image Segmentation ◽  
Imaging Systems

Abstract Medical image analysis is performed by analyzing images obtained by medical imaging systems to solve clinical problems. The purpose is to extract effective information and improve the level of clinical diagnosis. In recent years, automatic segmentation based on deep learning (DL) methods has been widely used, where a neural network can automatically learn image features, which is in sharp contrast with the traditional manual learning method. U-net is one of the most important semantic segmentation frameworks for a convolutional neural network (CNN). It is widely used in the medical image analysis domain for lesion segmentation, anatomical segmentation, and classification. The advantage of this network framework is that it can not only accurately segment the desired feature target and effectively process and objectively evaluate medical images but also help to improve accuracy in the diagnosis by medical images. Therefore, this article presents a literature review of medical image segmentation based on U-net, focusing on the successful segmentation experience of U-net for different lesion regions in six medical imaging systems. Along with the latest advances in DL, this article introduces the method of combining the original U-net architecture with deep learning and a method for improving the U-net network.

scholarly journals Review Research of Medical Image Analysis Using Deep Learning

2020 ◽  
Vol 4 (2) ◽  
pp. 75
Author(s):  
Bakhtyar Ahmed Mohammed ◽  
Muzhir Shaban Al-Ani
Keyword(s):  
Image Analysis ◽  
Deep Learning ◽  
Magnetic Resonance ◽  
Medical Imaging ◽  
Intelligent Systems ◽  
Medical Image ◽  
Medical Image Analysis ◽  
Medical Image Segmentation ◽  
Emission Computed Tomography ◽  
Resonance Spectroscopy

In modern globe, medical image analysis significantly participates in diagnosis process. In general, it involves five processes, such as medical image classification, medical image detection, medical image segmentation, medical image registration, and medical image localization. Medical imaging uses in diagnosis process for most of the human body organs, such as brain tumor, chest, breast, colonoscopy, retinal, and many other cases relate to medical image analysis using various modalities. Multi-modality images include magnetic resonance imaging, single photon emission computed tomography (CT), positron emission tomography, optical coherence tomography, confocal laser endoscopy, magnetic resonance spectroscopy, CT, X-ray, wireless capsule endoscopy, breast cancer, papanicolaou smear, hyper spectral image, and ultrasound use to diagnose different body organs and cases. Medical image analysis is appropriate environment to interact with automate intelligent system technologies. Among the intelligent systems deep learning (DL) is the modern one to manipulate medical image analysis processes and processing an image into fundamental components to extract meaningful information. The best model to establish its systems is deep convolutional neural network. This study relied on reviewing of some of these studies because of these reasons; improvements of medical imaging increase demand on automate systems of medical image analysis using DL, in most tested cases, accuracy of intelligent methods especially DL methods higher than accuracy of hand-crafted works. Furthermore, manually works need a lot of time compare to systematic diagnosis.

scholarly journals DMCNN: A Deep Multiscale Convolutional Neural Network Model for Medical Image Segmentation

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Lin Teng ◽  
Hang Li ◽  
Shahid Karim
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Image Segmentation ◽  
Convolutional Neural Network ◽  
Medical Image ◽  
Data Augmentation ◽  
Medical Images ◽  
Medical Image Segmentation ◽  
Related Area ◽  
Segmentation Accuracy

Medical image segmentation is one of the hot issues in the related area of image processing. Precise segmentation for medical images is a vital guarantee for follow-up treatment. At present, however, low gray contrast and blurred tissue boundaries are common in medical images, and the segmentation accuracy of medical images cannot be effectively improved. Especially, deep learning methods need more training samples, which lead to time-consuming process. Therefore, we propose a novelty model for medical image segmentation based on deep multiscale convolutional neural network (CNN) in this article. First, we extract the region of interest from the raw medical images. Then, data augmentation is operated to acquire more training datasets. Our proposed method contains three models: encoder, U-net, and decoder. Encoder is mainly responsible for feature extraction of 2D image slice. The U-net cascades the features of each block of the encoder with those obtained by deconvolution in the decoder under different scales. The decoding is mainly responsible for the upsampling of the feature graph after feature extraction of each group. Simulation results show that the new method can boost the segmentation accuracy. And, it has strong robustness compared with other segmentation methods.

scholarly journals Survey on deep learning for pulmonary medical imaging

2019 ◽  
Vol 14 (4) ◽  
pp. 450-469 ◽  
Author(s):  
Jiechao Ma ◽  
Yang Song ◽  
Xi Tian ◽  
Yiting Hua ◽  
Rongguo Zhang ◽  
...  
Keyword(s):  
Image Analysis ◽  
Deep Learning ◽  
Medical Imaging ◽  
Language Processing ◽  
Medical Image ◽  
Medical Image Analysis ◽  
Rapid Evolution ◽  
Comprehensive Overview ◽  
Feature Representations ◽  
Learning Techniques

AbstractAs a promising method in artificial intelligence, deep learning has been proven successful in several domains ranging from acoustics and images to natural language processing. With medical imaging becoming an important part of disease screening and diagnosis, deep learning-based approaches have emerged as powerful techniques in medical image areas. In this process, feature representations are learned directly and automatically from data, leading to remarkable breakthroughs in the medical field. Deep learning has been widely applied in medical imaging for improved image analysis. This paper reviews the major deep learning techniques in this time of rapid evolution and summarizes some of its key contributions and state-of-the-art outcomes. The topics include classification, detection, and segmentation tasks on medical image analysis with respect to pulmonary medical images, datasets, and benchmarks. A comprehensive overview of these methods implemented on various lung diseases consisting of pulmonary nodule diseases, pulmonary embolism, pneumonia, and interstitial lung disease is also provided. Lastly, the application of deep learning techniques to the medical image and an analysis of their future challenges and potential directions are discussed.

Deep Learning for Medical Image Segmentation

2021 ◽  
pp. 40-77
Author(s):  
Kanchan Sarkar ◽  
Bohang Li
Keyword(s):  
Image Segmentation ◽  
Deep Learning ◽  
Medical Imaging ◽  
Medical Image ◽  
Disease Diagnosis ◽  
Medical Image Segmentation ◽  
Kidney Tumour ◽  
Tumour Segmentation ◽  
Recent Innovation ◽  
Segmentation Algorithms

Pixel accurate 2-D, 3-D medical image segmentation to identify abnormalities for further analysis is on high demand for computer-aided medical imaging applications. Various segmentation algorithms have been studied and applied in medical imaging for many years, but the problem remains challenging due to growing a large number of variety of applications starting from lung disease diagnosis based on x-ray images, nucleus detection, and segmentation based on microscopic pictures to kidney tumour segmentation. The recent innovation in deep learning brought revolutionary advances in computer vision. Image segmentation is one such area where deep learning shows its capacity and improves the performance by a larger margin than its successor. This chapter overviews the most popular deep learning-based image segmentation techniques and discusses their capabilities and basic advantages and limitations in the domain of medical imaging.

Artificial Intelligence and Big Data

2020 ◽  
Vol 237 (12) ◽  
pp. 1438-1441
Author(s):  
Soenke Langner ◽  
Ebba Beller ◽  
Felix Streckenbach
Keyword(s):  
Artificial Intelligence ◽  
Image Analysis ◽  
Big Data ◽  
Deep Learning ◽  
Medical Image ◽  
Medical Images ◽  
Medical Image Analysis ◽  
Clinical Applications ◽  
Radiological Imaging ◽  
Learning Techniques

AbstractMedical images play an important role in ophthalmology and radiology. Medical image analysis has greatly benefited from the application of “deep learning” techniques in clinical and experimental radiology. Clinical applications and their relevance for radiological imaging in ophthalmology are presented.

scholarly journals Medical Imaging using Deep Learning Models

2021 ◽  
Vol 6 (5) ◽  
pp. 156-167
Author(s):  
Chetanpal Singh
Keyword(s):  
Neural Networks ◽  
Image Analysis ◽  
Deep Learning ◽  
Medical Imaging ◽  
Medical Image ◽  
Potential Role ◽  
Medical Image Analysis ◽  
Learning Models ◽  
Quality Healthcare ◽  
Learning Techniques

Deep learning has played a potential role in quality healthcare with fast automated and proper medical image analysis. In clinical applications, medical imaging is one of the most important parameters as with the help of this; experts can detect, monitor, and diagnose any kind of problems that are there in the patient's body. However, there are two things that one needs to understand; that is, the implementation of Artificial Neural Networks and Convolutional Neural Networks as well as deep learning to know about medical image analysis. It is necessary to state here that the deep learning approach is gaining attention in the medical imaging field in evaluating the presence or absence of disease in a patient. Mammography images, digital histopathology images, computerized tomography, etc. are some of the areas on which DL implementation focuses. One upon going through the paper will get to know the recent development that has occurred in this field and come up with a critical review on this aspect. The paper has demonstrated in detail modern deep learning models that are implemented in medical image analysis. There is no doubt about the promising future of the deep learning models and according to experts; the implementation of deep learning techniques has outperformed medical experts in numerous tasks. However, deep learning also has some drawbacks and challenges that are required to be addressed like limited datasets and many more. To mitigate such kinds of challenges, researchers are working on this aspect so that they can enhance healthcare by deploying AI.

Demystification of Deep Learning-Driven Medical Image Processing and Its Impact on Future Biomedical Applications

2020 ◽  
pp. 155-171
Author(s):  
R. Udendhran ◽  
Balamurugan M.
Keyword(s):  
Image Analysis ◽  
Deep Learning ◽  
Medical Imaging ◽  
Medical Image ◽  
Biomedical Applications ◽  
Medical Image Analysis ◽  
Learning Algorithms ◽  
New Era ◽  
Fast Development ◽  
Computational Technology

The recent growth of big data has ushered in a new era of deep learning algorithms in every sphere of technological advance, including medicine, as well as in medical imaging, particularly radiology. However, the recent achievements of deep learning, in particular biomedical applications, have, to some extent, masked decades-long developments in computational technology for medical image analysis. The methods of multi-modality medical imaging have been implemented in clinical as well as research studies. Due to the reason that multi-modal image analysis and deep learning algorithms have seen fast development and provide certain benefits to biomedical applications, this chapter presents the importance of deep learning-driven medical imaging applications, future advancements, and techniques to enhance biomedical applications by employing deep learning.

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 Comparative Study of Different Technique for Medical Image Segmentation: A Survey

2013 ◽  
Vol 11 (1) ◽  
pp. 2169-2174
Author(s):  
Abhishek Chandale ◽  
Divakar Singh
Keyword(s):  
Image Analysis ◽  
Image Segmentation ◽  
Medical Image ◽  
Object Representation ◽  
Medical Images ◽  
User Interaction ◽  
Relevant Information ◽  
Medical Image Segmentation ◽  
Segmentation Of Images ◽  
Review Current

Image analysis and processing can be done using image segmentation as one of the most critical aspect. Various subsequent processes of image analysis such as object representation object description ,feature measurement get affected as a consequence of segmentation .Other higher level tasks such as object classification also get affected b y segmentation. Due to all above reasons, imagesegmentation is considered as one of the most vital and essential process for providing features like delineration, characterization and visualization of important regions in any medical image. Its is necessary to provide accuracy with less time consumption in medical image segmentation Since manual segmentation is very tedious and time consuming to achieve and medical images are mostly unmanageable to be examined .Hence it is necessary to analysis & review current methods and algorithms accruing and need minimal user interaction in case of medical images. In medical images segmentation, the anatomical structure or region to be examined has to be extracted and delineated so as to improve its visibility. This paper projects importance of decision making process in segmentation of images to extract relevant information.

scholarly journals When medical images meet generative adversarial network: recent development and research opportunities

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Xiang Li ◽  
Yuchen Jiang ◽  
Juan J. Rodriguez-Andina ◽  
Hao Luo ◽  
Shen Yin ◽  
...  
Keyword(s):  
Image Analysis ◽  
Deep Learning ◽  
Medical Image ◽  
Medical Images ◽  
Medical Image Analysis ◽  
Real Data ◽  
Image Synthesis ◽  
Future Research ◽  
Generative Adversarial Network ◽  
Adversarial Network

AbstractDeep learning techniques have promoted the rise of artificial intelligence (AI) and performed well in computer vision. Medical image analysis is an important application of deep learning, which is expected to greatly reduce the workload of doctors, contributing to more sustainable health systems. However, most current AI methods for medical image analysis are based on supervised learning, which requires a lot of annotated data. The number of medical images available is usually small and the acquisition of medical image annotations is an expensive process. Generative adversarial network (GAN), an unsupervised method that has become very popular in recent years, can simulate the distribution of real data and reconstruct approximate real data. GAN opens some exciting new ways for medical image generation, expanding the number of medical images available for deep learning methods. Generated data can solve the problem of insufficient data or imbalanced data categories. Adversarial training is another contribution of GAN to medical imaging that has been applied to many tasks, such as classification, segmentation, or detection. This paper investigates the research status of GAN in medical images and analyzes several GAN methods commonly applied in this area. The study addresses GAN application for both medical image synthesis and adversarial learning for other medical image tasks. The open challenges and future research directions are also discussed.

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