Medical Image Analysis Applications Research Articles

A review of deep learning and Generative Adversarial Networks applications in medical image analysis

Nowadays, computer-aided decision support systems (CADs) for the analysis of images have been a perennial technique in the medical imaging field. In CADs, deep learning algorithms are widely used to perform tasks like classification, identification of patterns, detection, etc. Deep learning models learn feature representations from images rather than handcrafted features. Hence, deep learning models are quickly becoming the state-of-the-art method to achieve good performances in different computer-aided decision-support systems in medical applications. Similarly, deep learning-based generative models called Generative Adversarial Networks (GANs) have recently been developed as a novel method to produce realistic-looking synthetic data. GANs are used in different domains, including medical imaging generation. The common problems, like class imbalance and a small dataset, in healthcare are well addressed by GANs, and it is a leading area of research. Segmentation, reconstruction, detection, denoising, registration, etc. are the important applications of GANs. So in this work, the successes of deep learning methods in segmentation, classification, cell structure and fracture detection, computer-aided identification, and GANs in synthetic medical image generation, segmentation, reconstruction, detection, denoising, and registration in recent times are reviewed. Lately, the review article concludes by raising research directions for DL models and GANs in medical applications.

Open-source graphical user interface for the creation of synthetic skeletons for medical image analysis

Open-source graphical user interface for the creation of synthetic skeletons for medical image analysis

Cross and local optimal avoidance of RIME algorithm: A segmentation study for COVID-19 X-ray images

Image segmentation is a crucial technique in analyzing X-ray medical images as it aids in uncovering relevant information concealed within a patient's body, a pivotal aspect of the diagnostic process. The effectiveness of computer-aided diagnosis systems largely depends on the accuracy of the image processing methods. In recent years, multi-threshold image segmentation methods have found widespread application in medical image analysis. Despite the effectiveness of some renowned methods for binary threshold segmentation, the field still faces challenges due to the high cost of threshold computation. Metaheuristic algorithms hold the potential to address this issue as they can produce sufficiently reasonable solutions with manageable computational overheads. While some similar methods have been proposed, the imbalance between exploration and exploitation results in instability as the number of thresholds increases. Consequently, these solutions suffer from reduced efficiency in computing thresholds. In this study, a variant of the latest RIME algorithm, termed SLCRIME, is proposed. This paper replaces the pseudo-random method with low-discrepancy Sobol sequences for solution initialization. Additionally, two methods aimed at avoiding local optima and promoting information exchange within the solution set are introduced, further enhancing its capability to search for optimal threshold sets for IS systems. Subsequently, a multi-threshold image segmentation model based on SLCRIME is proposed and applied to segment 6 COVID-19 X-ray images. In the experiments, SLCRIME is compared with 6 peer algorithms, and the results are evaluated using image segmentation accuracy, feature similarity index metrics (FSIM), peak signal-to-noise ratio (PSNR), and structural similarity index metrics (SSIM). The analysis indicates that SLCRIME achieves optimal thresholds at reasonable computational costs and outperforms other algorithms in terms of performance.

Cancerous and Non-Cancerous MRI Classification Using Dual DCNN Approach.

Brain cancer is a life-threatening disease requiring close attention. Early and accurate diagnosis using non-invasive medical imaging is critical for successful treatment and patient survival. However, manual diagnosis by radiologist experts is time-consuming and has limitations in processing large datasets efficiently. Therefore, efficient systems capable of analyzing vast amounts of medical data for early tumor detection are urgently needed. Deep learning (DL) with deep convolutional neural networks (DCNNs) emerges as a promising tool for understanding diseases like brain cancer through medical imaging modalities, especially MRI, which provides detailed soft tissue contrast for visualizing tumors and organs. DL techniques have become more and more popular in current research on brain tumor detection. Unlike traditional machine learning methods requiring manual feature extraction, DL models are adept at handling complex data like MRIs and excel in classification tasks, making them well-suited for medical image analysis applications. This study presents a novel Dual DCNN model that can accurately classify cancerous and non-cancerous MRI samples. Our Dual DCNN model uses two well-performed DL models, i.e., inceptionV3 and denseNet121. Features are extracted from these models by appending a global max pooling layer. The extracted features are then utilized to train the model with the addition of five fully connected layers and finally accurately classify MRI samples as cancerous or non-cancerous. The fully connected layers are retrained to learn the extracted features for better accuracy. The technique achieves 99%, 99%, 98%, and 99% of accuracy, precision, recall, and f1-scores, respectively. Furthermore, this study compares the Dual DCNN's performance against various well-known DL models, including DenseNet121, InceptionV3, ResNet architectures, EfficientNetB2, SqueezeNet, VGG16, AlexNet, and LeNet-5, with different learning rates. This study indicates that our proposed approach outperforms these established models in terms of performance.

Deep Learning in Medical Image Analysis Article Review

Transfer learning, in evaluation to common deep studying strategies which include convolutional neural networks (CNNs), stands proud due to its simplicity, efficiency, and coffee education value, efficaciously addressing the venture of restricted datasets. The importance of scientific picture analysis in both scientific research and medical prognosis can't be overstated, with image techniques like Computer Tomography (CT), Magnetic Resonance Image (MRI), Ultrasound (US), and X-Ray playing a crucial function. Despite their utility in non-invasive analysis, the scarcity of categorized medical images poses a completely unique challenge in comparison to datasets in other pc imaginative and prescient domains, like facial reputation.
 Given this shortage, switch getting to know has won reputation amongst researchers for medical photo processing. This complete evaluation draws on one hundred amazing papers from IEEE, Elsevier, Google Scholar, Web of Science, and diverse sources spanning 2000 to 2023 It covers vital components, which includes the (i) shape of CNNs, (ii) foundational know-how of switch learning, (iii) numerous techniques for enforcing transfer mastering, (iv) the utility of switch gaining knowledge of throughout numerous sub-fields of medical photo analysis, and (v) a dialogue at the future potentialities of transfer studying within the realm of medical image analysis. This evaluate no longer handiest equips beginners with a scientific understanding of transfer mastering applications in medical image analysis but additionally serves policymakers by means of summarizing the evolving trends in transfer learning within the scientific image domain. This insight might also encourage policymakers to formulate advantageous rules that support the continued development of Transfer learning knowledge of in medical image analysis.

Independently Trained Multi-Scale Registration Network Based on Image Pyramid.

Image registration is a fundamental task in various applications of medical image analysis and plays a crucial role in auxiliary diagnosis, treatment, and surgical navigation. However, cardiac image registration is challenging due to the large non-rigid deformation of the heart and the complex anatomical structure. To address this challenge, this paper proposes an independently trained multi-scale registration network based on an image pyramid. By down-sampling the original input image multiple times, we can construct image pyramid pairs, and design a multi-scale registration network using image pyramid pairs of different resolutions as the training set. Using image pairs of different resolutions, train each registration network independently to extract image features from the image pairs at different resolutions. During the testing stage, the large deformation registration is decomposed into a multi-scale registration process. The deformation fields of different resolutions are fused by a step-by-step deformation method, thereby addressing the challenge of directly handling large deformations. Experiments were conducted on the open cardiac dataset ACDC (Automated Cardiac Diagnosis Challenge); the proposed method achieved an average Dice score of 0.828 in the experimental results. Through comparative experiments, it has been demonstrated that the proposed method effectively addressed the challenge of heart image registration and achieved superior registration results for cardiac images.

Deep Learning-Based Medical Image Registration Algorithm: Enhancing Accuracy with Dense Connections and Channel Attention Mechanisms

In critical clinical medical image analysis applications, such as surgical navigation and tumor monitoring, image registration is crucial. Recognizing the potential for enhanced accuracy in existing unsupervised image registration techniques for single-modal imagery, this research introduces an innovative deep learning-based image registration algorithm. Its novelty resides in integrating short and long connections to create a densely connected structure, markedly refining the feature map interconnectivity within the U-Net architecture. This advancement addresses the significant semantic gap issues arising from disparities in feature map sampling depths. Moreover, the algorithm incorporates a channel attention mechanism within the U-shaped network's decoder, significantly mitigating image noise and facilitating the generation of smoother deformation fields. This enhancement not only boosts the model's detail sensitivity but also markedly increases image registration precision, particularly evident when processing single-modal brain MRI datasets, thereby proving the algorithm's efficacy and utility. Extensive clinical application-based training and testing have underscored this algorithm's substantial contributions to medical image registration accuracy enhancement. Overall, by leveraging deep learning technologies and innovative algorithmic structures, this study addresses pivotal challenges in medical image registration, offering more precise and dependable support for clinical applications like surgical navigation and tumor surveillance.

Application of deep-learning based computer vision in medical image analysis

The analyzing process of medical image has always been a crucial part for disease detection, diagnosis, surgery assisting and drug delivery. With the outbreak of population worldwide and the constant emergence of all types of complicated diseases, doctors nowadays are facing tremendous workloads and having difficulty in making precise diagnoses or performing more assured operations. Many experts have been striving to develop a wide range of techniques based on deep learning to ease doctors burden and improve patients recovery process or survival chances. This general review paper will mainly focus on providing readers with a brief understanding and knowledge about deep-learning based computer vision applied in modern medical image analysis. Several academic journals or books related to computer vision applications in medical image analysis were selected for review. Despite the advantages and convenience of this technique, this review finds out that potential obstacles still exist and can be overcome or amended in the future.

Structure-aware independently trained multi-scale registration network for cardiac images.

Image registration is a primary task in various medical image analysis applications. However, cardiac image registration is difficult due to the large non-rigid deformation of the heart and the complex anatomical structure. This paper proposes a structure-aware independently trained multi-scale registration network (SIMReg) to address this challenge. Using image pairs of different resolutions, independently train each registration network to extract image features of large deformation image pairs at different resolutions. In the testing stage, the large deformation registration is decomposed into a multi-scale registration process, and the deformation fields of different resolutions are fused by a step-by-step deformation method, thus solving the difficulty of directly processing large deformation. Meanwhile, the targeted introduction of MIND (modality independent neighborhood descriptor) structural features to guide network training enhances the registration of cardiac structural contours and improves the registration effect of local details. Experiments were carried out on the open cardiac dataset ACDC (automated cardiac diagnosis challenge), and the average Dice value of the experimental results of the proposed method was 0.833. Comparative experiments showed that the proposed SIMReg could better solve the problem of heart image registration and achieve a better registration effect on cardiac images.

Identification method of thyroid nodule ultrasonography based on self-supervised learning dual-branch attention learning framework.

Thyroid ultrasound is a widely used diagnostic technique for thyroid nodules in clinical practice. However, due to the characteristics of ultrasonic imaging, such as low image contrast, high noise levels, and heterogeneous features, detecting and identifying nodules remains challenging. In addition, high-quality labeled medical imaging datasets are rare, and thyroid ultrasound images are no exception, posing a significant challenge for machine learning applications in medical image analysis. In this study, we propose a Dual-branch Attention Learning (DBAL) convolutional neural network framework to enhance thyroid nodule detection by capturing contextual information. Leveraging jigsaw puzzles as a pretext task during network training, we improve the network's generalization ability with limited data. Our framework effectively captures intrinsic features in a global-to-local manner. Experimental results involve self-supervised pre-training on unlabeled ultrasound images and fine-tuning using 1216 clinical ultrasound images from a collaborating hospital. DBAL achieves accurate discrimination of thyroid nodules, with a 88.5% correct diagnosis rate for malignant and benign nodules and a 93.7% area under the ROC curve. This novel approach demonstrates promising potential in clinical applications for its accuracy and efficiency.

DeepPyramid+: medical image segmentation using Pyramid View Fusion and Deformable Pyramid Reception

PurposeSemantic segmentation plays a pivotal role in many applications related to medical image and video analysis. However, designing a neural network architecture for medical image and surgical video segmentation is challenging due to the diverse features of relevant classes, including heterogeneity, deformability, transparency, blunt boundaries, and various distortions. We propose a network architecture, DeepPyramid+, which addresses diverse challenges encountered in medical image and surgical video segmentation.MethodsThe proposed DeepPyramid+ incorporates two major modules, namely “Pyramid View Fusion” (PVF) and “Deformable Pyramid Reception” (DPR), to address the outlined challenges. PVF replicates a deduction process within the neural network, aligning with the human visual system, thereby enhancing the representation of relative information at each pixel position. Complementarily, DPR introduces shape- and scale-adaptive feature extraction techniques using dilated deformable convolutions, enhancing accuracy and robustness in handling heterogeneous classes and deformable shapes.ResultsExtensive experiments conducted on diverse datasets, including endometriosis videos, MRI images, OCT scans, and cataract and laparoscopy videos, demonstrate the effectiveness of DeepPyramid+ in handling various challenges such as shape and scale variation, reflection, and blur degradation. DeepPyramid+ demonstrates significant improvements in segmentation performance, achieving up to a 3.65% increase in Dice coefficient for intra-domain segmentation and up to a 17% increase in Dice coefficient for cross-domain segmentation.ConclusionsDeepPyramid+ consistently outperforms state-of-the-art networks across diverse modalities considering different backbone networks, showcasing its versatility. Accordingly, DeepPyramid+ emerges as a robust and effective solution, successfully overcoming the intricate challenges associated with relevant content segmentation in medical images and surgical videos. Its consistent performance and adaptability indicate its potential to enhance precision in computerized medical image and surgical video analysis applications.

Anatomical Landmark Detection in 3d MRI Scan using Deep Neuro-Dynamic Programming

Automatic and precise biometric measures are crucial for diagnosis and supporting important decisions in medical imaging. For several applications of medical image analysis, automatically identifying anatomical landmarks is an important step. In this study, we determine the effectiveness of deep-neurodynamic programming methods for developing agents that can consistently and accurately recognize ground truth landmarks in medical imaging. Through interaction with an environment of three-dimensional (3D) scan ultrasound images, a simulated reinforcement learning (RL) agent learns to identify the optimum path leading to the landmark. Deep Q-Network (DQN) algorithm is applied to magnetic resonance imaging (MRI) images of the human brain. The model’s effectiveness and localization error are measured in terms of the mean Euclidean distance between the detected and ground truth landmarks’ position. We achieved a low localization error of 3.55mm during our experiments with the ADNI dataset.

RegFSC-Net: Medical Image Registration via Fourier Transform With Spatial Reorganization and Channel Refinement Network.

Medical image registration is crucial in medical image analysis applications. Recently, U-Net-style networks have been commonly used for unsupervised image registration, predicting dense displacement fields in full-resolution space. However, this process is resource-intensive and time-consuming for high-resolution volumetric image data. To address this challenge, this paper proposes a novel model named RegFSC-Net, which utilizes Fourier transform with spatial reorganization (SR) and channel refinement (CR) network for registration. We embed efficient feature extraction modules SR and CR modules into the encoder, and adopt a parameter-free model to drive the decoder to improve the U-shaped network. Precisely, RegFSC-Net does not directly predict the full-resolution displacement field in space but learns the low-dimensional representation of the displacement field in the bandlimited Fourier domain, which is beneficial in reducing network parameters, memory usage, and computational costs. Experimental results show that RegFSC-Net outperforms various state-of-the-art methods. Specifically, in comparison to the widely recognized Transformer-based method TransMorph, RegFSC-Net utilizes only around 8.2% of its parameters, resulting in a 1.95% higher Dice score and significantly faster inference speeds of 126.67% and 419.99% on GPU and CPU, respectively. Furthermore, we also designed three variants of RegFSC-Net and demonstrated their potential applications in computer-aided diagnosis.

A Comprehensive Survey of Machine Learning Applications in Medical Image Analysis for Artificial Vision

This study presents a thorough survey of the applications of machine learning in medical image analysis for artificial vision,aiming to offer a comprehensive understanding of the evolving intersection between machine learning and medical imaging.With the rapid advancement of artificial vision technologies, the integration of machine learning algorithms has becomepivotal in revolutionizing medical image analysis. The survey explores a diverse range of machine learning applicationswithin the medical imaging domain, encompassing techniques such as convolutional neural networks (CNNs), support vectormachines, and decision trees. The focus lies in elucidating the role of machine learning in enhancing the accuracy, efficiency,and diagnostic capabilities of medical image analysis systems. Key topics addressed in the survey include image segmentation, classification, and detection, with a specific emphasis on applications in radiology, pathology, and ophthalmology. Additionally, the survey discusses challenges and opportunities in the integration of machine learning into medical image analysis, providing insights into current trends and future directions. This comprehensive survey serves as a valuable resource for researchers, practitioners, and healthcare professionals seeking an in-depth overview of the diverse applications and evolving landscape of machine learning in medical image analysis for artificial vision.

Deep Learning in Barrett's Esophagus Diagnosis: Current Status and Future Directions.

Barrett's esophagus (BE) represents a pre-malignant condition characterized by abnormal cellular proliferation in the distal esophagus. A timely and accurate diagnosis of BE is imperative to prevent its progression to esophageal adenocarcinoma, a malignancy associated with a significantly reduced survival rate. In this digital age, deep learning (DL) has emerged as a powerful tool for medical image analysis and diagnostic applications, showcasing vast potential across various medical disciplines. In this comprehensive review, we meticulously assess 33 primary studies employing varied DL techniques, predominantly featuring convolutional neural networks (CNNs), for the diagnosis and understanding of BE. Our primary focus revolves around evaluating the current applications of DL in BE diagnosis, encompassing tasks such as image segmentation and classification, as well as their potential impact and implications in real-world clinical settings. While the applications of DL in BE diagnosis exhibit promising results, they are not without challenges, such as dataset issues and the "black box" nature of models. We discuss these challenges in the concluding section. Essentially, while DL holds tremendous potential to revolutionize BE diagnosis, addressing these challenges is paramount to harnessing its full capacity and ensuring its widespread application in clinical practice.

Polyp Identification from a Colonoscopy Image Using Semantic Segmentation Approach

Colorectal Cancer (CRC) is a major contributor to cancer-related mortality worldwide, necessitating early detection and treatment of polyps to prevent cancer progression. A colonoscopy is a critical diagnostic procedure for identifying colon abnormalities and removing premalignant polyps. However, accurately segmenting polyps in colonoscopy images poses challenges due to their diverse appearance and indistinct boundaries. In this study, we investigate augmentation techniques to enhance polyp semantic segmentation using the U-Net model. Our analysis reveals that the most effective technique is found in sub-scenario 2.6.c with an input size of 320×320, striking a favorable balance between accuracy and efficiency. Additionally, we explore the benefits of larger input sizes, taking into account resource considerations. Moreover, we conduct further testing of the best augmentation technique identified in previous experiments with the SegNet model. The results show a 3.5% improvement in the dice coefficient and slightly better qualitative outcomes. However, it is important to note that this enhancement comes with a nearly fivefold increase in training time. Moving forward, our objective is to develop a unified model for segmenting diverse medical images, pushing the boundaries of polyp detection and medical imaging. This research provides valuable insights and lays the foundation for more advanced applications in polyp detection and medical image analysis.

MODELING AND ENHANCING LOW-QUALITY RETINAL FUNDUS IMAGES

Retinal Fundus images are widely used for the clinical screening and diagnosis of eye diseases. However, fundus images captured by operators with various levels of experience have a large variation in quality. Low-quality fundus images increase uncertainty in clinical observation and lead to the risk of misdiagnosis. However, due to the special optical beam of fundus imaging and structure of the retina, natural image enhancement methods cannot be utilized directly to address this. The proposed system analyzed the main contribution is a new set of shape features, called Dynamic Shape Features, which do not require precise segmentation of the regions to be classified. Then, based on the degradation model, a clinically oriented fundus enhancement network (cofe-Net) is proposed to suppress global degradation factors, while simultaneously preserving anatomical retinal structures and pathological characteristics for clinical observation and analysis. Moreover, the system show that the fundus correction method can benefit medical image analysis applications, e.g., retinal vessel segmentation and optic disc/cup detection. Key Words: retinal fundus, retinal vessel segmentation, dynamic shape features, optic disc/cup detection

A Novel Gradient-Weighted Voting Approach for Classical and Fuzzy Circular Hough Transforms and Their Application in Medical Image Analysis—Case Study: Colonoscopy

Classical circular Hough transform was proven to be effective for some types of colorectal polyps. However, the polyps are very rarely perfectly circular, so some tolerance is needed, that can be ensured by applying fuzzy Hough transform instead of the classical one. In addition, the edge detection method, which is used as a preprocessing step of the Hough transforms, was changed from the generally used Canny method to Prewitt that detects fewer edge points outside of the polyp contours and also a smaller number of points to be transformed based on statistical data from three colonoscopy databases. According to the statistical study we performed, in the colonoscopy images the polyp contours usually belong to gradient domain of neither too large, nor too small gradients, though they can also have stronger or weaker segments. In order to prioritize the gradient domain typical for the polyps, a relative gradient-based thresholding as well as a gradient-weighted voting was introduced in this paper. For evaluating the improvement of the shape deviation tolerance of the classical and fuzzy Hough transforms, the maximum radial displacement and the average radius were used to characterize the roundness of the objects to be detected. The gradient thresholding proved to decrease the calculation time to less than 50% of the full Hough transforms, and the number of the resulting circles outside the polyp’s environment also decreased, especially for low resolution images.

Federated Learning for Medical Image Analysis with Deep Neural Networks.

Medical image analysis using deep neural networks (DNN) has demonstrated state-of-the-art performance in image classification and segmentation tasks, aiding disease diagnosis. The accuracy of the DNN is largely governed by the quality and quantity of the data used to train the model. However, for the medical images, the critical security and privacy concerns regarding sharing of local medical data across medical establishments precludes exploiting the full DNN potential for clinical diagnosis. The federated learning (FL) approach enables the use of local model's parameters to train a global model, while ensuring data privacy and security. In this paper, we review the federated learning applications in medical image analysis with DNNs, highlight the security concerns, cover some efforts to improve FL model performance, and describe the challenges and future research directions.

Few-shot multi-modal registration with mono-modal knowledge transfer

Multi-modal registration is a key problem in many medical image analysis applications. Recent learning-based deformable image registration methods become attractive alternatives to traditional methods because of their great performance and fast run time. However, their success relies on large training datasets that are rarely available in multi-modal registration scenarios. To address this, we propose a novel knowledge transfer-based network (KT-Net) for few-shot multi-modal registration, which focuses on transferring knowledge of the mono-modal registration model to multi-modal registration. The contributions can be two-fold: (1) we propose model decoupling to disentangle the registration model into a feature learning network and an alignment learning network. The two networks are trained on large mono-modal datasets, making preparations for knowledge transfer. (2) a reverse teaching strategy is further designed to align the features of multi-modal images with a few samples, enabling the knowledge from mono-modal registration to transfer to multi-modal registration. Experimental results on multi-contrast brain MRI datasets demonstrate that our proposed method yields accurate and robust registration performance under the constraint of a few multi-modal samples. Compared with state-of-the-art registration methods, our proposed method achieves better registration performance with a high average Dice score of up to 83.5% and an average 95% percentile of Hausdorff distance as low as 1.26mm in various anatomical structures, showing great potential for mono-modal knowledge transfer to be applied in few-shot multi-modal registration.