Generative adversarial networks and its applications in the biomedical image segmentation: a comprehensive survey (original) (raw)
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Generative Adversarial Networks (GANs) in Medical Imaging: Advancements, Applications and Challenges
IEEE access, 2024
Generative Adversarial Networks are a class of artificial intelligence algorithms that consist of a generator and a discriminator trained simultaneously through adversarial training. GANs have found crucial applications in various fields, including medical imaging. In healthcare, GANs contribute by generating synthetic medical images, enhancing data quality, and aiding in image segmentation, disease detection, and medical image synthesis. Their importance lies in their ability to generate realistic images, facilitating improved diagnostics, research, and training for medical professionals. Understanding its applications, algorithms, current advancements, and challenges is imperative for further advancement in the medical imaging domain. However, no study explores the recent state-of-the-art development of GANs in medical imaging. To overcome this research gap, in this extensive study, we began by exploring the vast array of applications of GANs in medical imaging, scrutinizing them within recent research. We then dive into the prevalent datasets and pre-processing techniques to enhance comprehension. Subsequently, an in-depth discussion of the GAN algorithms, elucidating their respective strengths and limitations, is provided. After that, we meticulously analyzed the results and experimental details of some recent cutting-edge research to obtain a more comprehensive understanding of the current development of GANs in medical imaging. Lastly, we discussed the diverse challenges encountered and future research directions to mitigate these concerns. This systematic review offers a complete overview of GANs in medical imaging, encompassing their application domains, models, state-of-the-art results analysis, challenges, and research directions, serving as a valuable resource for multidisciplinary studies.
IEEE Access, 2020
The recent significant increase in accuracy of medical image processing is attributed to the use of deep neural networks as manual segmentation generates errors in interpretation besides, is very arduous and inefficient. Generative adversarial networks (GANs) is a particular interest to medical researchers, as it implements adversarial loss without explicit modeling of the probability density function. Medical image segmentation methods face challenges of generalization and over-fitting, as medical data suffers from various shapes and diversity of organs. Furthermore, generating a sufficiently large annotated dataset at a clinical site is costly. To generalize learning with a small amount of training data, we propose guided GANs (GGANs) that can decimate samples from an input image and guide networks to generate images and corresponding segmentation mask. The decimated sampling is the key element of the proposed method employed to reduce network size using only a few parameters. Moreover, this method yields promising results by generating several outputs, such as bagging approach. Furthermore, errors of loss function increase, during the generation of original images and corresponding segmentation mask, in comparison to generating only the segmentation mask. Minimization of increased error leads (GGANs) to enhance the performance of segmentation using smaller datasets and less testing time. This method can be applied to a wide range of segmentation problems for different modalities and various organs (such as aortic root, left atrium, knee cartilage, and brain tumors) during a real-time crisis in hospitals. The proposed network also yields high accuracy compared to state-of-the-art networks. INDEX TERMS Medical image segmentation, generative adversarial network, aortic valve, left atrium, knee cartilage, brain tumor, decimated sample.
Generative Adversarial Network for Medical Images (MI-GAN
Deep learning algorithms produces state-of-the-art results for different machine learning and computer vision tasks. To perform well on a given task, these algorithms require large dataset for training. However, deep learning algorithms lack generalization and suffer from over-fitting whenever trained on small dataset, especially when one is dealing with medical images. For supervised image analysis in medical imaging, having image data along with their corresponding annotated ground-truths is costly as well as time consuming since annotations of the data is done by medical experts manually. In this paper, we propose a new Generative Adversarial Network for Medical Imaging (MI-GAN). The MI-GAN generates synthetic medical images and their segmented masks, which can then be used for the application of supervised analysis of medical images. Particularly , we present MI-GAN for synthesis of retinal images. The proposed method generates precise segmented images better than the existing techniques. The proposed model achieves a dice coefficient of 0.837 on STARE dataset and 0.832 on DRIVE dataset which is state-of-the-art performance on both the datasets.
Discover Artificial Intelligence, 2021
Deep 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.
Generative adversarial network in medical imaging: A review
Medical Image Analysis, 2019
Generative adversarial networks have gained a lot of attention in the computer vision community due to their capability of data generation without explicitly modelling the probability density function. The adversarial loss brought by the discriminator provides a clever way of incorporating unlabeled samples into training and imposing higher order consistency. This has proven to be useful in many cases, such as domain adaptation, data augmentation, and image-to-image translation. These properties have attracted researchers in the medical imaging community, and we have seen rapid adoption in many traditional and novel applications, such as image reconstruction, segmentation, detection, classification, and cross-modality synthesis. Based on our observations, this trend will continue and we therefore conducted a review of recent advances in medical imaging using the adversarial training scheme with the hope of benefiting researchers interested in this technique.
A GAN based Framework for Multi-Modal Medical Image Segmentation
Image segmentation is the procedure of dividing a digital image into a multiple set of pixels. The prior goal of the segmentation is to make things simpler and transform the representation of medical images into a meaningful subject. Many variant modalities, such as CT, X-ray, MRI, microscopy, positron emission tomography, single photon emission computer tomography, among others, make segmentation difficult. The challenging problem is for segmenting the regions with missing edges, absence of texture contrast, region of interest (ROI), and background. Most of the current researches only focuses on single-mode or paired multimodal images, and there are few researches on single-mode processing of unpaired multimodal images (Unified multimodal), the latter is more flexible and has good generalization ability in processing medical images. Based on the analysis of the existing medical image problems, this paper focuses on the unified multimodal segmentation of medical images by leveraging General Adversarial Network (GAN). GAN provides a simple and effective paradigm for image generation, which has been increasingly used in different fields such as migration learning and data enhancement. In this research, we have proposed a GAN based framework for addressing the issues concerning multi-modal medical image segmentation.
A Novel Generative Adversarial Network-Based Approach for Automated Brain Tumour Segmentation
Medicina
Background: Medical image segmentation is more complicated and demanding than ordinary image segmentation due to the density of medical pictures. A brain tumour is the most common cause of high mortality. Objectives: Extraction of tumorous cells is particularly difficult due to the differences between tumorous and non-tumorous cells. In ordinary convolutional neural networks, local background information is restricted. As a result, previous deep learning algorithms in medical imaging have struggled to detect anomalies in diverse cells. Methods: As a solution to this challenge, a deep convolutional generative adversarial network for tumour segmentation from brain Magnetic resonance Imaging (MRI) images is proposed. A generator and a discriminator are the two networks that make up the proposed model. This network focuses on tumour localisation, noise-related issues, and social class disparities. Results: Dice Score Coefficient (DSC), Peak Signal to Noise Ratio (PSNR), and Structural I...
ArXiv, 2019
Recently, generative adversarial networks exhibited excellent performances in semi-supervised image analysis scenarios. In this paper, we go even further by proposing a fully unsupervised approach for segmentation applications with prior knowledge of the objects' shapes. We propose and investigate different strategies to generate simulated label data and perform image-to-image translation between the image and the label domain using an adversarial model. Specifically, we assess the impact of the annotation model's accuracy as well as the effect of simulating additional low-level image features. For experimental evaluation, we consider the segmentation of the glomeruli, an application scenario from renal pathology. Experiments provide proof of concept and also confirm that the strategy for creating the simulated label data is of particular relevance considering the stability of GAN trainings.
A Review of Generative Adversarial Networks in Cancer Imaging: New Applications, New Solutions
ArXiv, 2021
Despite technological and medical advances, the detection, interpretation, and treatment of cancer based on imaging data continue to pose significant challenges. These include high inter-observer variability, difficulty of small-sized lesion detection, nodule interpretation and malignancy determination, interand intra-tumour heterogeneity, class imbalance, segmentation inaccuracies, and treatment effect uncertainty. The recent advancements in Generative Adversarial Networks (GANs) in computer vision as well as in medical imaging may provide a basis for enhanced capabilities in cancer detection and analysis. In this review, we assess the potential of GANs to address a number of key challenges of cancer imaging, including data scarcity and imbalance, domain and dataset shifts, data access and privacy, data annotation and quantification, as well as cancer detection, tumour profiling and treatment planning. We provide a critical appraisal of the existing literature of GANs applied to ca...
On the effectiveness of GAN generated cardiac MRIs for segmentation
ArXiv, 2020
In this work, we propose a Variational Autoencoder (VAE) - Generative Adversarial Networks (GAN) model that can produce highly realistic MRI together with its pixel accurate groundtruth for the application of cine-MR image cardiac segmentation. On one side of our model is a Variational Autoencoder (VAE) trained to learn the latent representations of cardiac shapes. On the other side is a GAN that uses "SPatially-Adaptive (DE)Normalization" (SPADE) modules to generate realistic MR images tailored to a given anatomical map. At test time, the sampling of the VAE latent space allows to generate an arbitrary large number of cardiac shapes, which are fed to the GAN that subsequently generates MR images whose cardiac structure fits that of the cardiac shapes. In other words, our system can generate a large volume of realistic yet labeled cardiac MR images. We show that segmentation with CNNs trained with our synthetic annotated images gets competitive results compared to traditio...