Deep Learning in Cardiology

The medical field is creating large amount of data that physicians are unable to decipher and use efficiently. Moreover, rule-based expert systems are inefficient in solving complicated medical tasks or for creating insights using big data. Deep learning has emerged as a more accurate and effective technology in a wide range of medical problems such as diagnosis, prediction and intervention. Deep learning is a representation learning method that consists of layers that transform the data non-linearly, thus, revealing hierarchical relationships and structures. In this review we survey deep learning application papers that use structured data, signal and imaging modalities from cardiology. We discuss the advantages and limitations of applying deep learning in cardiology that also apply in medicine in general, while proposing certain directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table

Angle-Closure Detection in Anterior Segment OCT based on Multi-Level Deep Network

Irreversible visual impairment is often caused by primary angle-closure glaucoma, which could be detected via Anterior Segment Optical Coherence Tomography (AS-OCT). In this paper, an automated system based on deep learning is presented for angle-closure detection in AS-OCT images. Our system learns a discriminative representation from training data that captures subtle visual cues not modeled by handcrafted features. A Multi-Level Deep Network (MLDN) is proposed to formulate this learning, which utilizes three particular AS-OCT regions based on clinical priors: the global anterior segment structure, local iris region, and anterior chamber angle (ACA) patch. In our method, a sliding window based detector is designed to localize the ACA region, which addresses ACA detection as a regression task. Then, three parallel sub-networks are applied to extract AS-OCT representations for the global image and at clinically-relevant local regions. Finally, the extracted deep features of these sub-networks are concatenated into one fully connected layer to predict the angle-closure detection result. In the experiments, our system is shown to surpass previous detection methods and other deep learning systems on two clinical AS-OCT datasets.Comment: 9 pages, accepted by IEEE Transactions on Cybernetic

400 kHz Spectral Domain Optical Coherence Tomography for Corneal Imaging

The cornea is the transparent, outermost layer of the human eye that contributes approximately 70% of the refractive power of the eye in air. It is composed of five major tissue layers: the epithelium, the Bowman’s membrane, the stroma, the Descemet’s membrane, and the endothelium. Corneal diseases such as Keratoconus and Fuchs’ dystrophy can change the morphology of some or all of the corneal layers, which can lead to vision impairment and eventually blindness. For example, Keratoconus causes localizes thinning and thickening of the corneal epithelium, damage to the collagen structure of the corneal stroma (scarring) and alteration of the corneal curvature. All of these changes result in blurred and double vision, and in severe cases can lead to corneal blindness that would require corneal replacement surgery. Fuchs’ dystrophy is a genetic disease that damages the endothelium cell. Since the endothelial cells are responsible for maintaining the fluid level in the stroma, impairment or death of the endothelial cells leads to dehydration or edema of the cornea that results in partial or full corneal blindness. Systemic diseases such as diabetes also affect the physiology and morphology of the cornea. Diabetes affects all the corneal cells and leads to abnormalities such as neuropathy, keratopathy, stromal edema, decrease in endothelial cell density, low tear secretion etc. Although there have been many clinical studies of these diseases, knowledge of the early-stage changes in the corneal morphology at the cellular level remains unclear. Understanding the early stage of disease development with the help of high speed and ultra-high resolution optical coherence tomography (UHR-OCT) corneal imaging can improve the early diagnostics of corneal diseases and well as monitoring the effectiveness of different therapies such as surgical intervention or administration of pharmaceutical drugs. The main objectives of my research project were: a) to upgrade the 34 kHz OCT system with a new camera that offered a 400 kHz data acquisition rate and 8192-pixel linear array sensor, b) test the performance of the 400 kHz OCT system for ex-vivo and in-vivo corneal imaging, and c) develop pre-processing for the interferogram and post-processing algorithms for the images. Implementing a camera with a faster acquisition rate will help to reduce the motion artifact caused by involuntary eye motions. Also, compared to 4500 pixels used in the 34 kHz camera, the new system utilizes approximately 7500 pixels, resulting in a larger scanning range. Although new camera has smaller sensor size (30% smaller), vertical binning is applied to ensure the light signal is all captured. However, due to the faster acquisition rate (~11 times faster), about 10 dB of SNR will suffers from the reduced integration time. Doubling the sample arm power while keep all other conditions the same can boost the SNR by about 3 dB. Therefore, incident power at the sample arm will be raised carefully according to the maximum permissible exposure calculated using the American National Standard for Ophthalmics – Light Hazard Protection for Ophthalmics instruments provided by ANSI. The result from the technical tests shows that the 400 kHz SD-OCT system offers 1 µm axial resolution in biological tissue with an extended scanning range of 2.8 mm (compared to 1.2 mm of the 34 kHz system). It has a lateral resolution of 1.04 μm/pix and can resolve group 7 element 6 of the USAF target with a 20x objective. It can provide 83 dB SNR with 0.95 mW of incident power at a 400 kHz image acquisition rate which should be sufficient to image semi-transparent biological tissues such as the human retina and cornea. However, given the much higher image acquisition rate (> 10x higher), the imaging power can be increased safely to ~ 4 mW, which will increase the system’s SNR to ~ 90 dB. So far, the performance of the 400 kHz OCT system has been tested by imaging plant tissues (cucumber) and ex-vivo pig corneas, due to the cancellation of all in-vivo human and animal studies imposed by COVID-19

CAD system for early diagnosis of diabetic retinopathy based on 3D extracted imaging markers.

This dissertation makes significant contributions to the field of ophthalmology, addressing the segmentation of retinal layers and the diagnosis of diabetic retinopathy (DR). The first contribution is a novel 3D segmentation approach that leverages the patientspecific anatomy of retinal layers. This approach demonstrates superior accuracy in segmenting all retinal layers from a 3D retinal image compared to current state-of-the-art methods. It also offers enhanced speed, enabling potential clinical applications. The proposed segmentation approach holds great potential for supporting surgical planning and guidance in retinal procedures such as retinal detachment repair or macular hole closure. Surgeons can benefit from the accurate delineation of retinal layers, enabling better understanding of the anatomical structure and more effective surgical interventions. Moreover, real-time guidance systems can be developed to assist surgeons during procedures, improving overall patient outcomes. The second contribution of this dissertation is the introduction of a novel computeraided diagnosis (CAD) system for precise identification of diabetic retinopathy. The CAD system utilizes 3D-OCT imaging and employs an innovative approach that extracts two distinct features: first-order reflectivity and 3D thickness. These features are then fused and used to train and test a neural network classifier. The proposed CAD system exhibits promising results, surpassing other machine learning and deep learning algorithms commonly employed in DR detection. This demonstrates the effectiveness of the comprehensive analysis approach employed by the CAD system, which considers both low-level and high-level data from the 3D retinal layers. The CAD system presents a groundbreaking contribution to the field, as it goes beyond conventional methods, optimizing backpropagated neural networks to integrate multiple levels of information effectively. By achieving superior performance, the proposed CAD system showcases its potential in accurately diagnosing DR and aiding in the prevention of vision loss. In conclusion, this dissertation presents novel approaches for the segmentation of retinal layers and the diagnosis of diabetic retinopathy. The proposed methods exhibit significant improvements in accuracy, speed, and performance compared to existing techniques, opening new avenues for clinical applications and advancements in the field of ophthalmology. By addressing future research directions, such as testing on larger datasets, exploring alternative algorithms, and incorporating user feedback, the proposed methods can be further refined and developed into robust, accurate, and clinically valuable tools for diagnosing and monitoring retinal diseases

Image-to-image translation with Generative Adversarial Networks via retinal masks for realistic Optical Coherence Tomography imaging of Diabetic Macular Edema disorders

[Abstract]: One of the main issues with deep learning is the need of a significant number of samples. We intend to address this problem in the field of Optical Coherence Tomography (OCT), specifically in the context of Diabetic Macular Edema (DME). This pathology represents one of the main causes of blindness in developed countries and, due to the capturing difficulties and saturation of health services, the task of creating computer-aided diagnosis (CAD) systems is an arduous task. For this reason, we propose a solution to generate samples. Our strategy employs image-to-image Generative Adversarial Networks (GAN) to translate a binary mask into a realistic OCT image. Moreover, thanks to the clinical relationship between the retinal shape and the presence of DME fluid, we can generate both pathological and non-pathological samples by altering the binary mask morphology. To demonstrate the capabilities of our proposal, we test it against two classification strategies of the state-of-the-art. In the first one, we evaluate a system fully trained with generated images, obtaining 94.83% accuracy with respect to the state-of-the-art. In the second case, we tested it against a state-of-the-art expert model based on deep features, in which it also achieved successful results with a 98.23% of the accuracy of the original work. This way, our methodology proved to be useful in scenarios where data is scarce, and could be easily adapted to other imaging modalities and pathologies where key shape constraints in the image provide enough information to recreate realistic samples

Exploring Deep Learning Techniques for Glaucoma Detection: A Comprehensive Review

Glaucoma is one of the primary causes of vision loss around the world, necessitating accurate and efficient detection methods. Traditional manual detection approaches have limitations in terms of cost, time, and subjectivity. Recent developments in deep learning approaches demonstrate potential in automating glaucoma detection by detecting relevant features from retinal fundus images. This article provides a comprehensive overview of cutting-edge deep learning methods used for the segmentation, classification, and detection of glaucoma. By analyzing recent studies, the effectiveness and limitations of these techniques are evaluated, key findings are highlighted, and potential areas for further research are identified. The use of deep learning algorithms may significantly improve the efficacy, usefulness, and accuracy of glaucoma detection. The findings from this research contribute to the ongoing advancements in automated glaucoma detection and have implications for improving patient outcomes and reducing the global burden of glaucoma

Texture analysis and Its applications in biomedical imaging: a survey

Texture analysis describes a variety of image analysis techniques that quantify the variation in intensity and pattern. This paper provides an overview of several texture analysis approaches addressing the rationale supporting them, their advantages, drawbacks, and applications. This survey’s emphasis is in collecting and categorising over five decades of active research on texture analysis.Brief descriptions of different approaches are presented along with application examples. From a broad range of texture analysis applications, this survey’s final focus is on biomedical image analysis. An up-to-date list of biological tissues and organs in which disorders produce texture changes that may be used to spot disease onset and progression is provided. Finally, the role of texture analysis methods as biomarkers of disease is summarised.Manuscript received February 3, 2021; revised June 23, 2021; accepted September 21, 2021. Date of publication September 27, 2021; date of current version January 24, 2022. This work was supported in part by the Portuguese Foundation for Science and Technology (FCT) under Grants PTDC/EMD-EMD/28039/2017, UIDB/04950/2020, PestUID/NEU/04539/2019, and CENTRO-01-0145-FEDER-000016 and by FEDER-COMPETE under Grant POCI-01-0145-FEDER-028039. (Corresponding author: Rui Bernardes.)info:eu-repo/semantics/publishedVersio