Download or read book Equivariance For Deep Learning And Retinal Imaging written by Daniel Ernest Worrall and published by . This book was released on 2019 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Analysing and generalizing from small datasets is still a difficult challenge. However, the de facto model in computer vision, the convolutional neural network (CNN), is known to be data hungry and poorly interpretable. We investigate a new design principle called equivariance, which we demonstrate can both improve data efficiency and interpretability in CNNs. Intermediate layers of an equivariant CNN preserve information about local and global transformations of the input image. Moreover, we build specific equivariances into a CNN, such that they need not be learned from data. As a result, less data is needed, improving the network generalization. In this thesis, we explore three techniques to demonstrate the usefulness of equivariance. We start by considering equivariance to 3D right-angle rotations in the context of voxelized data. We show that this improves classification performance on the ModelNet10 dataset, among non-ensembled models. We then introduce Harmonic Networks, which are equivariant to continuous rotations in 2D. These exploit the fact that the Fourier transform naturally disentangles transformation and magnitude information. To the best of our knowledge, this is the first CNN architecture exhibiting equivariance to a continuous transformation. Lastly we consider learning equivariance from data, to model equivariances, which may not be tractably designed into a network. To validate our work, we harness a real-world case study by applying some of our developments to the difficult challenge of Retinopathy of Prematurity (ROP) detection. ROP is a sight-threatening disease, affecting 60% of premature babies below 32 weeks post-menstrual age and 1500g in weight. ROP is difficult to detect, its symptomatology is not fully understood, and doctors all over the world are increasingly burdened with screening, as the number of at-risk neonates sharply increases. To the best of our knowledge, we are the first to develop a deep learning solution to ROP detection.

Download or read book Computational Retinal Image Analysis written by Emanuele Trucco and published by Academic Press. This book was released on 2019-11-19 with total page 506 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computational Retinal Image Analysis: Tools, Applications and Perspectives gives an overview of contemporary retinal image analysis (RIA) in the context of healthcare informatics and artificial intelligence. Specifically, it provides a history of the field, the clinical motivation for RIA, technical foundations (image acquisition modalities, instruments), computational techniques for essential operations, lesion detection (e.g. optic disc in glaucoma, microaneurysms in diabetes) and validation, as well as insights into current investigations drawing from artificial intelligence and big data. This comprehensive reference is ideal for researchers and graduate students in retinal image analysis, computational ophthalmology, artificial intelligence, biomedical engineering, health informatics, and more. Provides a unique, well-structured and integrated overview of retinal image analysis Gives insights into future areas, such as large-scale screening programs, precision medicine, and computer-assisted eye care Includes plans and aspirations of companies and professional bodies

Download or read book Deep Learning Based Multimodal Retinal Image Processing written by Yiqian Wang and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The retina, the light sensitive tissue lining the interior of the eye, is the only part of the central nervous system (CNS) that can be imaged at micron resolution in vivo. Retinal diseases including age-related macular degeneration, diabetes retinopathy, and vascular occlusions are important causes of vision loss and have systemic implications for millions of patients. Retinal imaging is of great significance to diagnosing and monitoring both retinal diseases and systematic diseases that manifest in the retina. A variety of imaging devices have been developed, including color fundus (CF) photography, infrared reflectance (IR), fundus autofluorescence (FAF), dye-based angiography, optical coherence tomography (OCT), and OCT angiography (OCT-A). Each imaging modality is particularly useful for observing certain aspects of the retina, and can be utilized for visualization of specific diseases. In this dissertation, we propose deep learning based methods for retinal image processing, including multimodal retinal image registration, OCT motion correction, and OCT retinal layer segmentation. We present our established work on a deep learning framework for multimodal retinal image registration, a comprehensive study of the correlation between subjective and objective evaluation metrics for multimodal retinal image registration, convolutional neural networks for correction of axial and coronal motion artifacts in 3D OCT volumes, and joint motion correction and 3D OCT layer segmentation network. The dissertation not only proposes novel approaches in image processing, enhances the observation of retinal diseases, but will also provide insights on observing systematic diseases through the retina, including diabetes, cardiovascular disease, and preclinical Alzheimer's Disease. The proposed deep learning based retinal image processing approaches would build a connection between ophthalmology and image processing literature, and the findings may provide a good insight for researchers who investigate retinal image registration, retinal image segmentation and retinal disease detection.

Download or read book Symbiotic Registration and Deep Learning for Retinal Image Analysis written by Li Ding (Electrical and computer engineering researcher) and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Geometry and semantics are two sub-fields in computer vision that have been researched extensively as two separate problems over decades. However, semantics and geometry in computer vision are not mutually exclusive and techniques developed for one could complement the other. Unfortunately, the interplay of these two fields has received limited attention. In this thesis, we design symbiotic geometric and semantic computer vision methods in the specific context of retinal image analysis, where we consider the semantic problem of retinal vessel detection and the geometric problem of retinal image registration. First, we propose a novel pipeline for vessel detection in fluorescein angiography (FA) using deep neural networks (DNNs) that reduces the effort required for labeling ground truth data by combining cross-modality transfer and human-in-the-loop learning. The cross-modality transfer exploits concurrently captured color fundus (CF) and fundus FA. Binary vessels maps detected from CF images with a pre-trained network are geometrically registered with FA images via robust parametric chamfer alignment. Using the transferred vessels as initial ground truth labels, the human-in-the-loop approach progressively improves the ground truth labeling by iterating between deep-learning and labeling. Experiments show that the proposed pipeline significantly reduces the annotation effort and outperforms prior FA vessel detection methods by a significant margin. Next, we describe an annotation-efficient deep learning framework for vessel detection in UWF fundus photography (FP) that does not require de novo labeled UWF FP vessel maps. Our approach uses concurrently captured UWF FA and iterates between a multi-modal registration step and a weakly-supervised learning step. In the registration step, UWF FA vessel maps detected with a pre-trained DNN are registered with the UWF FP via parametric chamfer alignment. The warped vessel maps are used as the tentative training data but inevitably contain incorrect labels due to the differences between the two modalities and the errors in the registration. In the learning step, a robust learning method is proposed to train DNNs with noisy labels. The registration and the vessel detection benefit from each other and are progressively improved. Results on two datasets show that the proposed approach provides accurate vessel detection, without requiring manually labeled UWF FP training data. Finally, we present a hybrid framework for registering retinal images in the presence of extreme geometric distortions that are commonly encountered in UWF FA. Our approach consists of a feature-based global registration and a vessel-based local refinement. For the global registration, we introduce a modified RANSAC algorithm that jointly identifies corresponding keypoints and estimates a polynomial geometric transformation consistent with the identified correspondences between reference and target images. Our RANSAC modification particularly improves feature matching and the registration in peripheral regions that are most severely impacted by the geometric distortions. The local refinement is formulated as a parametric chamfer alignment for vessel maps obtained using DNNs. Because the complete vessel maps contribute to the chamfer alignment, this approach not only improves registration accuracy but also aligns with clinical practice, where vessels are typically a key focus of examinations. Experiments conducted on two datasets show that the proposed framework significantly outperforms the existing retinal image registration methods"--Pages xv-xvii.

Download or read book Deep learning approach for the detection and quantification of intraretinal cystoid fluid in multivendor optical coherence tomography written by FREERK G. VENHUIZEN and published by Infinite Study. This book was released on with total page 25 pages. Available in PDF, EPUB and Kindle. Book excerpt: We developed a deep learning algorithm for the automatic segmentation and quantification of intraretinal cystoid fluid (IRC) in spectral domain optical coherence tomography (SD-OCT) volumes independent of the device used for acquisition.

Download or read book Retinal Image Analysis Based on Deep Learning written by B. Q. Al-Bander and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Multi Modal Retinal Image Registration Via Deep Neural Networks written by Junkang Zhang and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Multi-modal retinal images provide complementary anatomical information at various resolutions, color wavelengths, and fields of view. Aligning multi-modal images will establish a comprehensive view of the retina and benefit the screening and diagnosis of eye diseases. However, the inconsistent anatomical patterns across modalities create outliers in feature matching, and the lack of retinal boundaries may also fool the intensity-based alignment metrics, both of which will influence the alignment qualities. Besides, the varying distortion levels across Ultra-Widefield (UWF) and Narrow-Angle (NA) images, due to different camera parameters, will cause large alignment errors in global transformation. In addressing the issue of inconsistent patterns, we use retinal vasculature as a common signal for alignment. First, we build a two-step coarse-to-fine registration pipeline fully based on deep neural networks. The coarse alignment step estimates a global transformation via vessel segmentation, feature detection and description, and outlier rejection. While the fine alignment step corrects the remaining misalignment through deformable registration. In addition, we propose an unsupervised learning scheme based on style transfer to jointly train the networks for vessel segmentation and deformable registration. Finally, we also introduce Monogenical Phase signal as an alternative guidance in training the deformable registration network. Then, to deal with the issue of various distortion levels across UWF and NA modalities, we propose a distortion correction function to create images with similar distortion levels. Based on the assumptions of spherical eyeball shape and fixed UWF camera pose, the function reprojects the UWF pixels by an estimated correction camera with similar parameters as the NA camera. Besides, we incorporate the function into the coarse alignment networks which will simultaneously optimize the correction camera pose and refine the global alignment results. Moreover, to further reduce misalignment from the UWF-to-NA global registration, we estimate a 3D dense scene for the UWF pixels to represent a more flexible eyeball shape. Both the scene and the NA camera parameters are iteratively optimized to reduce the alignment error between the 3D-to-2D reprojected images and the original ones, which is also concatenated with the coarse alignment networks with distortion correction function.

Download or read book Deep Learning to See written by Alessandro Betti and published by Springer Nature. This book was released on 2022-04-26 with total page 116 pages. Available in PDF, EPUB and Kindle. Book excerpt: The remarkable progress in computer vision over the last few years is, by and large, attributed to deep learning, fueled by the availability of huge sets of labeled data, and paired with the explosive growth of the GPU paradigm. While subscribing to this view, this work criticizes the supposed scientific progress in the field, and proposes the investigation of vision within the framework of information-based laws of nature. This work poses fundamental questions about vision that remain far from understood, leading the reader on a journey populated by novel challenges resonating with the foundations of machine learning. The central thesis proposed is that for a deeper understanding of visual computational processes, it is necessary to look beyond the applications of general purpose machine learning algorithms, and focus instead on appropriate learning theories that take into account the spatiotemporal nature of the visual signal. Serving to inspire and stimulate critical reflection and discussion, yet requiring no prior advanced technical knowledge, the text can naturally be paired with classic textbooks on computer vision to better frame the current state of the art, open problems, and novel potential solutions. As such, it will be of great benefit to graduate and advanced undergraduate students in computer science, computational neuroscience, physics, and other related disciplines.

Download or read book Deep Learning and Data Labeling for Medical Applications written by Gustavo Carneiro and published by Springer. This book was released on 2016-10-07 with total page 289 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of two workshops held at the 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016, in Athens, Greece, in October 2016: the First Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, LABELS 2016, and the Second International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2016. The 28 revised regular papers presented in this book were carefully reviewed and selected from a total of 52 submissions. The 7 papers selected for LABELS deal with topics from the following fields: crowd-sourcing methods; active learning; transfer learning; semi-supervised learning; and modeling of label uncertainty.The 21 papers selected for DLMIA span a wide range of topics such as image description; medical imaging-based diagnosis; medical signal-based diagnosis; medical image reconstruction and model selection using deep learning techniques; meta-heuristic techniques for fine-tuning parameter in deep learning-based architectures; and applications based on deep learning techniques.

Download or read book Deep Learning for Medical Image Analysis written by S. Kevin Zhou and published by Academic Press. This book was released on 2023-12-01 with total page 544 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep Learning for Medical Image Analysis, Second Edition is a great learning resource for academic and industry researchers and graduate students taking courses on machine learning and deep learning for computer vision and medical image computing and analysis. Deep learning provides exciting solutions for medical image analysis problems and is a key method for future applications. This book gives a clear understanding of the principles and methods of neural network and deep learning concepts, showing how the algorithms that integrate deep learning as a core component are applied to medical image detection, segmentation, registration, and computer-aided analysis. · Covers common research problems in medical image analysis and their challenges · Describes the latest deep learning methods and the theories behind approaches for medical image analysis · Teaches how algorithms are applied to a broad range of application areas including cardiac, neural and functional, colonoscopy, OCTA applications and model assessment · Includes a Foreword written by Nicholas Ayache

Download or read book Medical Image Computing and Computer Assisted Intervention MICCAI 2022 written by Linwei Wang and published by Springer Nature. This book was released on 2022-09-15 with total page 775 pages. Available in PDF, EPUB and Kindle. Book excerpt: The eight-volume set LNCS 13431, 13432, 13433, 13434, 13435, 13436, 13437, and 13438 constitutes the refereed proceedings of the 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022, which was held in Singapore in September 2022. The 574 revised full papers presented were carefully reviewed and selected from 1831 submissions in a double-blind review process. The papers are organized in the following topical sections: Part I: Brain development and atlases; DWI and tractography; functional brain networks; neuroimaging; heart and lung imaging; dermatology; Part II: Computational (integrative) pathology; computational anatomy and physiology; ophthalmology; fetal imaging; Part III: Breast imaging; colonoscopy; computer aided diagnosis; Part IV: Microscopic image analysis; positron emission tomography; ultrasound imaging; video data analysis; image segmentation I; Part V: Image segmentation II; integration of imaging with non-imaging biomarkers; Part VI: Image registration; image reconstruction; Part VII: Image-Guided interventions and surgery; outcome and disease prediction; surgical data science; surgical planning and simulation; machine learning – domain adaptation and generalization; Part VIII: Machine learning – weakly-supervised learning; machine learning – model interpretation; machine learning – uncertainty; machine learning theory and methodologies.

Download or read book Research Anthology on Improving Medical Imaging Techniques for Analysis and Intervention written by Management Association, Information Resources and published by IGI Global. This book was released on 2022-09-09 with total page 1671 pages. Available in PDF, EPUB and Kindle. Book excerpt: Medical imaging provides medical professionals the unique ability to investigate and diagnose injuries and illnesses without being intrusive. With the surge of technological advancement in recent years, the practice of medical imaging has only been improved through these technologies and procedures. It is essential to examine these innovations in medical imaging to implement and improve the practice around the world. The Research Anthology on Improving Medical Imaging Techniques for Analysis and Intervention investigates and presents the recent innovations, procedures, and technologies implemented in medical imaging. Covering topics such as automatic detection, simulation in medical education, and neural networks, this major reference work is an excellent resource for radiologists, medical professionals, hospital administrators, medical educators and students, librarians, researchers, and academicians.

Download or read book Understanding the Biomarkers of Retinal Disease Using Deep Learning written by Malika Shahrawat and published by . This book was released on 2019 with total page 48 pages. Available in PDF, EPUB and Kindle. Book excerpt: The retina has biomarkers not only for ophthalmic disease but also for diseases and conditions across the entire body. In this paper, I focus on retinopathy of prematurity (ROP), a proliferative vascular disease that can cause blindness in prematurely born infants. To prevent further disease progression and vision loss in infants with ROP, early and accurate detection of plus disease is crucial. In current practice, clinicians compare retinal fundus photographs to a reference standard image in order to detect plus disease for severe ROP. This process can be highly qualitative, subjective, and variable. Furthermore, some clinical environments may lack clinicians with the expertise to diagnose these diseases. I am to address these shortcomings in current clinical diagnosis of ROP by using deep learning methods to automatically extract biomarkers of disease without human intervention. Since ROP is primarily present in prematurely born infants, I also attempt to predict and analyze gestational and postmenstrual age, and how disease predictions vary from healthy to affected infants.

Download or read book Automatic Retinal Image Analysis to Triage Retinal Pathologies written by Renoh Johnson Chalakkal and published by . This book was released on 2019 with total page 134 pages. Available in PDF, EPUB and Kindle. Book excerpt: Fundus retinal imaging is a non-invasive way of imaging the retina popular among the ophthalmic community and the targeted population. Over the past 15 years, extensive research and clinical studies using fundus images have been done for automatizing the screening and diagnosing process of three significant conditions affecting vision: macular edema, diabetic retinopathy, and glaucoma. These are the most important causes of preventable blindness around the globe, yet they can be successfully screened using the fundus image of the retina. Such diseases are associated with an observable variation in the structural and functional properties of the retina. Manual triage/diagnosis of these diseases is time-consuming and requires specialized ophthalmologists/optometrists; it is also expensive. Computer-aided medical triage/diagnosis can be applied to fundus retinal image analysis, thereby automatizing the triage. The process involves successfully combining sub-tasks focused at analyzing, locating, and segmenting different landmark structures inside a retina. The preliminary objective of this thesis is to develop automatic retinal image analysis (ARIA) techniques capable of analyzing, locating, and segmenting the key structures from the fundus image and combine them effectively to create a complete automatic screening system. First, the retinal vessel, which is the most important structure, is segmented. Two methods are developed for doing this: the first uses adaptive histogram equalization and anisotropic diffusion filtering, followed by weighted scaling and vessel edge enhancement. Fuzzy-C-mean classification, together with morphological transforms and connected component analysis, is applied to segment the vessel pixels. A second improved method for vessel segmentation is proposed, which is capable of segmenting the tiny peripheral vessel pixels missed by the first method. This method uses curvelet transform-based vessel edge enhancement technique followed by modified line operator-based vessel pixel segmentation. Second, a novel technique to automatically detect and segment important structures such as optic disc, macula, and fovea from a retinal image is developed. These structures, together with the retinal vessels, are considered as the retinal landmarks. The proposed method automatically detects the optic disc using histogram-based template matching combined with the maximum sum of vessel information. The optic disc region is segmented by using the Circular Hough Transform. For detecting fovea, the retinal image is uniformly divided into three horizontal stripes, and the strip including the detected optic disc, is selected. The contrast of the horizontal strip containing the optic disc region is then enhanced using a series of image processing steps. The macula region is first detected in the optic disc strip using various morphological operations and connected component analysis. The fovea is located inside this detected macular region. Next, an algorithm capable of analyzing the retinal image quality and content is developed. Often, methods focusing on ARIA use public retinal image databases for performance evaluation. The quality of images in such databases is often not evaluated as a pre-requisite for ARIA. Therefore, the performance metrics reported by such ARIA methods are inconsistent. Considering these facts, a deep learning-based approach to assess the quality of input retinal images is proposed. The method begins with a deep learning-based classification that identifies the image quality in terms of sharpness, illumination, and homogeneity, followed by an unsupervised second level that evaluates the field definition and content of the image. The proposed method is general and robust, making it more suitable than the alternative methods currently adopted in clinical practice. Finally, an automatic deep learning-based method for clinically significant macular edema triage is proposed. The classified high-quality retinal images are used as inputs. Both full image and ARIA processed image are experimented as the possible inputs. Deep convolutional neural networks are used as feature extractors. The extracted features are over-sampled to balance the highly skewed database samples across the examined classes. Finally, using the reduced feature set obtained through feature selection, a simple k-NN classifier demonstrates significant classification performance, thereby validating the preliminary objective of this study.

Download or read book A Combined Machine learning and Graph based Framework for the 3 D Automated Segmentation of Retinal Structures in SD OCT Images written by Bhavna Josephine Antony and published by . This book was released on 2013 with total page 143 pages. Available in PDF, EPUB and Kindle. Book excerpt: A machine-learning based approach was also used here to learn descriptive features of the NCO. Thus, the major contributions of this work include 1) a method for the automated correction of axial artifacts in SD-OCT images, 2) a combined machine-learning and graph-theoretic framework for the segmentation of retinal surfaces in SD-OCT images (applied to humans, mice and canines), 3) a novel formulation of the graph-theoretic approach for the segmentation of multiple surfaces and their shared hole (applied to the segmentation of the neural canal opening), and 4) the investigation of textural markers that could precede structural and functional change in degenerative retinal diseases.

Download or read book An Artificial Intelligence Framework for the Automated Segmentation and Quantitative Analysis of Retinal Vasculature written by Ali Hatamizadeh and published by . This book was released on 2020 with total page 55 pages. Available in PDF, EPUB and Kindle. Book excerpt: The reliable segmentation and quantification of retinal vasculature can provide the means to diagnose and monitor the progression of a variety of diseases affecting the blood vessel network, including diabetes and hypertension. In this thesis, we address this problem in depth, leveraging the power of artificial intelligence to devise automated approaches for the segmentation and width estimation of vessels in two ophthalmological image modalities. First, we investigate the automated segmentation of retinal vessels in color fundus images. We propose a novel, fully convolutional deep neural network with an encoder-decoder architecture that employs dilated spatial pyramid pooling with multiple dilation rates to recover the lost content in the encoder and add multiscale contextual information to the decoder. We also propose a simple yet effective way of quantifying and tracking the widths of retinal vessels through direct use of the segmentation predictions. The proposed methodology takes a whole-image approach and is tested on two publicly available datasets, DRIVE and CHASE-DB1. Second, we introduce the first deep-learning based method for the semantic segmentation of retinal arteries and veins in infrared imaging along with a novel dataset dubbed AVIR, and propose an innovative encoder-decoder that is regularized by variational autoencoders. Additionally, our method automatically quantifies the morphological changes of the segmented arteries and veins, which is important for establishing automated vessel tracking systems.

Download or read book Medical Image Computing and Computer Assisted Intervention MICCAI 2021 written by Marleen de Bruijne and published by Springer Nature. This book was released on 2021-09-22 with total page 658 pages. Available in PDF, EPUB and Kindle. Book excerpt: The eight-volume set LNCS 12901, 12902, 12903, 12904, 12905, 12906, 12907, and 12908 constitutes the refereed proceedings of the 24th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2021, held in Strasbourg, France, in September/October 2021.* The 531 revised full papers presented were carefully reviewed and selected from 1630 submissions in a double-blind review process. The papers are organized in the following topical sections: Part I: image segmentation Part II: machine learning - self-supervised learning; machine learning - semi-supervised learning; and machine learning - weakly supervised learning Part III: machine learning - advances in machine learning theory; machine learning - attention models; machine learning - domain adaptation; machine learning - federated learning; machine learning - interpretability / explainability; and machine learning - uncertainty Part IV: image registration; image-guided interventions and surgery; surgical data science; surgical planning and simulation; surgical skill and work flow analysis; and surgical visualization and mixed, augmented and virtual reality Part V: computer aided diagnosis; integration of imaging with non-imaging biomarkers; and outcome/disease prediction Part VI: image reconstruction; clinical applications - cardiac; and clinical applications - vascular Part VII: clinical applications - abdomen; clinical applications - breast; clinical applications - dermatology; clinical applications - fetal imaging; clinical applications - lung; clinical applications - neuroimaging - brain development; clinical applications - neuroimaging - DWI and tractography; clinical applications - neuroimaging - functional brain networks; clinical applications - neuroimaging – others; and clinical applications - oncology Part VIII: clinical applications - ophthalmology; computational (integrative) pathology; modalities - microscopy; modalities - histopathology; and modalities - ultrasound *The conference was held virtually.