Medical image segmentation using deep learning: A survey (original) (raw)
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A Survey on Medical Image Segmentation Based on Deep Learning Techniques
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Deep learning techniques have rapidly become important as a preferred method for evaluating medical image segmentation. This survey analyses different contributions in the deep learning medical field, including the major common issues published in recent years, and also discusses the fundamentals of deep learning concepts applicable to medical image segmentation. The study of deep learning can be applied to image categorization, object recognition, segmentation, registration, and other tasks. First, the basic ideas of deep learning techniques, applications, and frameworks are introduced. Deep learning techniques that operate the ideal applications are briefly explained. This paper indicates that there is a previous experience with different techniques in the class of medical image segmentation. Deep learning has been designed to describe and respond to various challenges in the field of medical image analysis such as low accuracy of image classification, low segmentation resolution,...
A Comprehensive Analysis of Medical Image Segmentation using Deep Learning
International Journal of Scientific Research and Review, 2019
Image segmentation has created important advances in recent years. Recent work construct to a great extent with respect to Deep Learning techniques that has brought about groundbreaking enhancement within the accuracy of segmentation. As a result of image segmentations are a midlevel illustration, they need a potential to create major contribution over the wide field of visual understanding from image classification and interactive pursuit. Medical image segmentation is a sub area of image segmentation that has many essential applications inside the prospect of medical image evaluation and diagnostic. In this paper, distinct strategies of medical image segmentation could be classified forthwith their sub techniques and sub fields. This paper presents useful approaches into the field of medical image segmentation using Deep Learning and attempt to summarize the long term scale of work.
A Study on Using Deep Learning for Segmentation of Medical Image
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Segmentation of medical images using deep learning has provided stateof-the-art performances in this area of work. With the availability of large digital datasets and access to powerful GPUs, deep learning has transformed our world. We are now able to make computers mimic and replicate the functions of the human mind simply by providing enough data and computing the problem. Deep learning has a huge potential for medical image analysis and now it has been firmly established as a robust tool in image segmentation. This paper addresses the six popular methods that have employed deep-learning techniques for the segmentation of medical images which play a massive impact in the medical healthcare industry and in turn make a contributing role towards the concept of smart cities. A comparative study on these deep learning-based segmentation techniques will provide a researcher working in the field of medical imaging to explore further in this area for higher accuracy and better results.
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Deep Learning for Medical Image Segmentation
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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.
Deep Learning for Image Segmentation: A Focus on Medical Imaging
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Image segmentation is crucial for various research areas. Many computer vision applications depend on segmenting images to understand the scene, such as autonomous driving, surveillance systems, robotics, and medical imaging. With the recent advances in deep learning (DL) and its confounding results in image segmentation, more attention has been drawn to its use in medical image segmentation. This article introduces a survey of the state-of-the-art deep convolution neural network (CNN) models and mechanisms utilized in image segmentation. First, segmentation models are categorized based on their model architecture and primary working principle. Then, CNN categories are described, and various models are discussed within each category. Compared with other existing surveys, several applications with multiple architectural adaptations are discussed within each category. A comparative summary is included to give the reader insights into utilized architectures in different applications and datasets. This study focuses on medical image segmentation applications, where the most widely used architectures are illustrated, and other promising models are suggested that have proven their success in different domains. Finally, the present work discusses current limitations and solutions along with future trends in the field.
Semantic Segmentation of Medical Images with Deep Learning: Overview
2020
Semantic segmentation is one of the biggest challenging tasks in computer vision, especially in medical image analysis, it helps to locate and identify pathological structures automatically. It is an active research area. Continuously different techniques are proposed. Recently Deep Learning is the latest technique used intensively to improve the performance in medical image segmentation. For this reason, we present in this non-systematic review a preliminary description about semantic segmentation with deep learning and the most important steps to build a model that deal with this problem.
Deep Neural Networks for Medical Image Segmentation
Journal of Healthcare Engineering, 2022
Image segmentation is a branch of digital image processing which has numerous applications in the field of analysis of images, augmented reality, machine vision, and many more. The field of medical image analysis is growing and the segmentation of the organs, diseases, or abnormalities in medical images has become demanding. The segmentation of medical images helps in checking the growth of disease like tumour, controlling the dosage of medicine, and dosage of exposure to radiations. Medical image segmentation is really a challenging task due to the various artefacts present in the images. Recently, deep neural models have shown application in various image segmentation tasks. This significant growth is due to the achievements and high performance of the deep learning strategies. This work presents a review of the literature in the field of medical image segmentation employing deep convolutional neural networks. The paper examines the various widely used medical image datasets, the ...
Medical image semantic segmentation based on deep learning (2)
The image semantic segmentation has been extensively studying. The modern methods rely on the deep convolutional neural networks, which can be trained to address this problem. A few years ago networks require the huge dataset to be trained. However, the recent advances in deep learning allow training networks on the small datasets, which is a critical issue for medical images, since the hospitals and research organizations usually do not provide the huge amount of data. In this paper, we address medical image semantic segmentation problem by applying the modern CNN model. Moreover, the recent achievements in deep learning allow processing the whole image per time by applying concepts of the fully convolutional neural network. Our qualitative and quantitate experiment results demonstrated that modern CNN can successfully tackle the medical image semantic segmentation problem.
Journal of Ambient Intelligence and Humanized Computing, 1-29, 2020. Q2 Journal.
Brain tumour identification with traditional magnetic resonance imaging (MRI) tends to be time-consuming and in most cases, reading of the resulting images by human agents is prone to error, making it desirable to use automated image segmentation. This is a multi-step process involving: (a) collecting data in the form of raw processed or raw images, (b) removing bias by using pre-processing, (c) processing the image and locating the brain tumour, and (d) showing the tumour affected areas on a computer screen or projector. Several systems have been proposed for medical image segmentation but have not been evaluated in the field. This may be due to ongoing issues of image clarity, grey and white matter present in a scan image, lack of knowledge of the end user and constraints arising from MRI imaging systems. This makes it imperative to develop a comprehensive technique for the accurate diagnosis of brain tumors in MRI images. In this paper, we introduce a taxonomy consisting of 'Data, Image segmentation processing, and View' (DIV) which are the major components required to develop a high-end system for brain tumour diagnosis based on deep learning neural networks. The DIV taxonomy is evaluated based on system completeness and acceptance. The utility of the DIV taxonomy is demonstrated by classifying 30 state-of-the-art publications in the domain of medFical image segmentation systems based on deep neural networks. The results demonstrate that few components of medical image segmentation systems have been validated although several have been evaluated by identifying role and efficiency of the components in this domain. Keywords Taxonomy · Medical image segmentation · Magnetic resonance imaging (MRI) · Brain tumour · Deep neural networks (DNN) · Diagnosis · Image contrast · Image clustering · Re-clustering · Image pixels · Tumour boundaries Abbreviations MRI Magnetic resonance imaging MCFM Modified fuzzy C-means CLE Confocal laser endomicroscopy CNN Convolutional neural networks DCNN Deep conventional neural network ACM Active contour models CRFs Conditional random fields FCNN Fully convolutional neural network LHNPSO Low-discrepancy sequence initialized particle swarm optimization algorithm with high-order nonlinear time-varying inertia weight KFECSB Kernelized fuzzy entropy clustering with spatial information and bias correction RF Classifier Random forests classifier