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<button class="button is-small is-link">Go</button> </div> </div> </form> </div> </div> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2405.09549">arXiv:2405.09549</a> <span> [<a href="https://arxiv.org/pdf/2405.09549">pdf</a>, <a href="https://arxiv.org/format/2405.09549">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> Deep-learning-based clustering of OCT images for biomarker discovery in age-related macular degeneration (Pinnacle study report 4) </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Holland%2C+R">Robbie Holland</a>, <a href="/search/eess?searchtype=author&query=Kaye%2C+R">Rebecca Kaye</a>, <a href="/search/eess?searchtype=author&query=Hagag%2C+A+M">Ahmed M. Hagag</a>, <a href="/search/eess?searchtype=author&query=Leingang%2C+O">Oliver Leingang</a>, <a href="/search/eess?searchtype=author&query=Taylor%2C+T+R+P">Thomas R. P. Taylor</a>, <a href="/search/eess?searchtype=author&query=Bogunovi%C4%87%2C+H">Hrvoje Bogunovi膰</a>, <a href="/search/eess?searchtype=author&query=Schmidt-Erfurth%2C+U">Ursula Schmidt-Erfurth</a>, <a href="/search/eess?searchtype=author&query=Scholl%2C+H+P+N">Hendrik P. N. Scholl</a>, <a href="/search/eess?searchtype=author&query=Rueckert%2C+D">Daniel Rueckert</a>, <a href="/search/eess?searchtype=author&query=Lotery%2C+A+J">Andrew J. Lotery</a>, <a href="/search/eess?searchtype=author&query=Sivaprasad%2C+S">Sobha Sivaprasad</a>, <a href="/search/eess?searchtype=author&query=Menten%2C+M+J">Martin J. Menten</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2405.09549v1-abstract-short" style="display: inline;"> Diseases are currently managed by grading systems, where patients are stratified by grading systems into stages that indicate patient risk and guide clinical management. However, these broad categories typically lack prognostic value, and proposals for new biomarkers are currently limited to anecdotal observations. In this work, we introduce a deep-learning-based biomarker proposal system for the… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.09549v1-abstract-full').style.display = 'inline'; document.getElementById('2405.09549v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2405.09549v1-abstract-full" style="display: none;"> Diseases are currently managed by grading systems, where patients are stratified by grading systems into stages that indicate patient risk and guide clinical management. However, these broad categories typically lack prognostic value, and proposals for new biomarkers are currently limited to anecdotal observations. In this work, we introduce a deep-learning-based biomarker proposal system for the purpose of accelerating biomarker discovery in age-related macular degeneration (AMD). It works by first training a neural network using self-supervised contrastive learning to discover, without any clinical annotations, features relating to both known and unknown AMD biomarkers present in 46,496 retinal optical coherence tomography (OCT) images. To interpret the discovered biomarkers, we partition the images into 30 subsets, termed clusters, that contain similar features. We then conduct two parallel 1.5-hour semi-structured interviews with two independent teams of retinal specialists that describe each cluster in clinical language. Overall, both teams independently identified clearly distinct characteristics in 27 of 30 clusters, of which 23 were related to AMD. Seven were recognised as known biomarkers already used in established grading systems and 16 depicted biomarker combinations or subtypes that are either not yet used in grading systems, were only recently proposed, or were unknown. Clusters separated incomplete from complete retinal atrophy, intraretinal from subretinal fluid and thick from thin choroids, and in simulation outperformed clinically-used grading systems in prognostic value. Overall, contrastive learning enabled the automatic proposal of AMD biomarkers that go beyond the set used by clinically established grading systems. Ultimately, we envision that equipping clinicians with discovery-oriented deep-learning tools can accelerate discovery of novel prognostic biomarkers. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.09549v1-abstract-full').style.display = 'none'; document.getElementById('2405.09549v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 12 March, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2308.05764">arXiv:2308.05764</a> <span> [<a href="https://arxiv.org/pdf/2308.05764">pdf</a>, <a href="https://arxiv.org/format/2308.05764">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Unlocking the Diagnostic Potential of ECG through Knowledge Transfer from Cardiac MRI </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Turgut%2C+%C3%96">脰zg眉n Turgut</a>, <a href="/search/eess?searchtype=author&query=M%C3%BCller%2C+P">Philip M眉ller</a>, <a href="/search/eess?searchtype=author&query=Hager%2C+P">Paul Hager</a>, <a href="/search/eess?searchtype=author&query=Shit%2C+S">Suprosanna Shit</a>, <a href="/search/eess?searchtype=author&query=Starck%2C+S">Sophie Starck</a>, <a href="/search/eess?searchtype=author&query=Menten%2C+M+J">Martin J. Menten</a>, <a href="/search/eess?searchtype=author&query=Martens%2C+E">Eimo Martens</a>, <a href="/search/eess?searchtype=author&query=Rueckert%2C+D">Daniel Rueckert</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2308.05764v1-abstract-short" style="display: inline;"> The electrocardiogram (ECG) is a widely available diagnostic tool that allows for a cost-effective and fast assessment of the cardiovascular health. However, more detailed examination with expensive cardiac magnetic resonance (CMR) imaging is often preferred for the diagnosis of cardiovascular diseases. While providing detailed visualization of the cardiac anatomy, CMR imaging is not widely availa… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2308.05764v1-abstract-full').style.display = 'inline'; document.getElementById('2308.05764v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2308.05764v1-abstract-full" style="display: none;"> The electrocardiogram (ECG) is a widely available diagnostic tool that allows for a cost-effective and fast assessment of the cardiovascular health. However, more detailed examination with expensive cardiac magnetic resonance (CMR) imaging is often preferred for the diagnosis of cardiovascular diseases. While providing detailed visualization of the cardiac anatomy, CMR imaging is not widely available due to long scan times and high costs. To address this issue, we propose the first self-supervised contrastive approach that transfers domain-specific information from CMR images to ECG embeddings. Our approach combines multimodal contrastive learning with masked data modeling to enable holistic cardiac screening solely from ECG data. In extensive experiments using data from 40,044 UK Biobank subjects, we demonstrate the utility and generalizability of our method. We predict the subject-specific risk of various cardiovascular diseases and determine distinct cardiac phenotypes solely from ECG data. In a qualitative analysis, we demonstrate that our learned ECG embeddings incorporate information from CMR image regions of interest. We make our entire pipeline publicly available, including the source code and pre-trained model weights. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2308.05764v1-abstract-full').style.display = 'none'; document.getElementById('2308.05764v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 9 August, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2308.00402">arXiv:2308.00402</a> <span> [<a href="https://arxiv.org/pdf/2308.00402">pdf</a>, <a href="https://arxiv.org/format/2308.00402">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> Metrics to Quantify Global Consistency in Synthetic Medical Images </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Scholz%2C+D">Daniel Scholz</a>, <a href="/search/eess?searchtype=author&query=Wiestler%2C+B">Benedikt Wiestler</a>, <a href="/search/eess?searchtype=author&query=Rueckert%2C+D">Daniel Rueckert</a>, <a href="/search/eess?searchtype=author&query=Menten%2C+M+J">Martin J. Menten</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2308.00402v1-abstract-short" style="display: inline;"> Image synthesis is increasingly being adopted in medical image processing, for example for data augmentation or inter-modality image translation. In these critical applications, the generated images must fulfill a high standard of biological correctness. A particular requirement for these images is global consistency, i.e an image being overall coherent and structured so that all parts of the imag… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2308.00402v1-abstract-full').style.display = 'inline'; document.getElementById('2308.00402v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2308.00402v1-abstract-full" style="display: none;"> Image synthesis is increasingly being adopted in medical image processing, for example for data augmentation or inter-modality image translation. In these critical applications, the generated images must fulfill a high standard of biological correctness. A particular requirement for these images is global consistency, i.e an image being overall coherent and structured so that all parts of the image fit together in a realistic and meaningful way. Yet, established image quality metrics do not explicitly quantify this property of synthetic images. In this work, we introduce two metrics that can measure the global consistency of synthetic images on a per-image basis. To measure the global consistency, we presume that a realistic image exhibits consistent properties, e.g., a person's body fat in a whole-body MRI, throughout the depicted object or scene. Hence, we quantify global consistency by predicting and comparing explicit attributes of images on patches using supervised trained neural networks. Next, we adapt this strategy to an unlabeled setting by measuring the similarity of implicit image features predicted by a self-supervised trained network. Our results demonstrate that predicting explicit attributes of synthetic images on patches can distinguish globally consistent from inconsistent images. Implicit representations of images are less sensitive to assess global consistency but are still serviceable when labeled data is unavailable. Compared to established metrics, such as the FID, our method can explicitly measure global consistency on a per-image basis, enabling a dedicated analysis of the biological plausibility of single synthetic images. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2308.00402v1-abstract-full').style.display = 'none'; document.getElementById('2308.00402v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 1 August, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2306.10941">arXiv:2306.10941</a> <span> [<a href="https://arxiv.org/pdf/2306.10941">pdf</a>, <a href="https://arxiv.org/format/2306.10941">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> Synthetic optical coherence tomography angiographs for detailed retinal vessel segmentation without human annotations </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Kreitner%2C+L">Linus Kreitner</a>, <a href="/search/eess?searchtype=author&query=Paetzold%2C+J+C">Johannes C. Paetzold</a>, <a href="/search/eess?searchtype=author&query=Rauch%2C+N">Nikolaus Rauch</a>, <a href="/search/eess?searchtype=author&query=Chen%2C+C">Chen Chen</a>, <a href="/search/eess?searchtype=author&query=Hagag%2C+A+M">Ahmed M. Hagag</a>, <a href="/search/eess?searchtype=author&query=Fayed%2C+A+E">Alaa E. Fayed</a>, <a href="/search/eess?searchtype=author&query=Sivaprasad%2C+S">Sobha Sivaprasad</a>, <a href="/search/eess?searchtype=author&query=Rausch%2C+S">Sebastian Rausch</a>, <a href="/search/eess?searchtype=author&query=Weichsel%2C+J">Julian Weichsel</a>, <a href="/search/eess?searchtype=author&query=Menze%2C+B+H">Bjoern H. Menze</a>, <a href="/search/eess?searchtype=author&query=Harders%2C+M">Matthias Harders</a>, <a href="/search/eess?searchtype=author&query=Knier%2C+B">Benjamin Knier</a>, <a href="/search/eess?searchtype=author&query=Rueckert%2C+D">Daniel Rueckert</a>, <a href="/search/eess?searchtype=author&query=Menten%2C+M+J">Martin J. Menten</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2306.10941v2-abstract-short" style="display: inline;"> Optical coherence tomography angiography (OCTA) is a non-invasive imaging modality that can acquire high-resolution volumes of the retinal vasculature and aid the diagnosis of ocular, neurological and cardiac diseases. Segmenting the visible blood vessels is a common first step when extracting quantitative biomarkers from these images. Classical segmentation algorithms based on thresholding are st… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2306.10941v2-abstract-full').style.display = 'inline'; document.getElementById('2306.10941v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2306.10941v2-abstract-full" style="display: none;"> Optical coherence tomography angiography (OCTA) is a non-invasive imaging modality that can acquire high-resolution volumes of the retinal vasculature and aid the diagnosis of ocular, neurological and cardiac diseases. Segmenting the visible blood vessels is a common first step when extracting quantitative biomarkers from these images. Classical segmentation algorithms based on thresholding are strongly affected by image artifacts and limited signal-to-noise ratio. The use of modern, deep learning-based segmentation methods has been inhibited by a lack of large datasets with detailed annotations of the blood vessels. To address this issue, recent work has employed transfer learning, where a segmentation network is trained on synthetic OCTA images and is then applied to real data. However, the previously proposed simulations fail to faithfully model the retinal vasculature and do not provide effective domain adaptation. Because of this, current methods are unable to fully segment the retinal vasculature, in particular the smallest capillaries. In this work, we present a lightweight simulation of the retinal vascular network based on space colonization for faster and more realistic OCTA synthesis. We then introduce three contrast adaptation pipelines to decrease the domain gap between real and artificial images. We demonstrate the superior segmentation performance of our approach in extensive quantitative and qualitative experiments on three public datasets that compare our method to traditional computer vision algorithms and supervised training using human annotations. Finally, we make our entire pipeline publicly available, including the source code, pretrained models, and a large dataset of synthetic OCTA images. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2306.10941v2-abstract-full').style.display = 'none'; document.getElementById('2306.10941v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 15 December, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 19 June, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Currently under review</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2301.04525">arXiv:2301.04525</a> <span> [<a href="https://arxiv.org/pdf/2301.04525">pdf</a>, <a href="https://arxiv.org/format/2301.04525">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> Clustering disease trajectories in contrastive feature space for biomarker discovery in age-related macular degeneration </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Holland%2C+R">Robbie Holland</a>, <a href="/search/eess?searchtype=author&query=Leingang%2C+O">Oliver Leingang</a>, <a href="/search/eess?searchtype=author&query=Holmes%2C+C">Christopher Holmes</a>, <a href="/search/eess?searchtype=author&query=Anders%2C+P">Philipp Anders</a>, <a href="/search/eess?searchtype=author&query=Kaye%2C+R">Rebecca Kaye</a>, <a href="/search/eess?searchtype=author&query=Riedl%2C+S">Sophie Riedl</a>, <a href="/search/eess?searchtype=author&query=Paetzold%2C+J+C">Johannes C. Paetzold</a>, <a href="/search/eess?searchtype=author&query=Ezhov%2C+I">Ivan Ezhov</a>, <a href="/search/eess?searchtype=author&query=Bogunovi%C4%87%2C+H">Hrvoje Bogunovi膰</a>, <a href="/search/eess?searchtype=author&query=Schmidt-Erfurth%2C+U">Ursula Schmidt-Erfurth</a>, <a href="/search/eess?searchtype=author&query=Fritsche%2C+L">Lars Fritsche</a>, <a href="/search/eess?searchtype=author&query=Scholl%2C+H+P+N">Hendrik P. N. Scholl</a>, <a href="/search/eess?searchtype=author&query=Sivaprasad%2C+S">Sobha Sivaprasad</a>, <a href="/search/eess?searchtype=author&query=Lotery%2C+A+J">Andrew J. Lotery</a>, <a href="/search/eess?searchtype=author&query=Rueckert%2C+D">Daniel Rueckert</a>, <a href="/search/eess?searchtype=author&query=Menten%2C+M+J">Martin J. Menten</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2301.04525v2-abstract-short" style="display: inline;"> Age-related macular degeneration (AMD) is the leading cause of blindness in the elderly. Current grading systems based on imaging biomarkers only coarsely group disease stages into broad categories and are unable to predict future disease progression. It is widely believed that this is due to their focus on a single point in time, disregarding the dynamic nature of the disease. In this work, we pr… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2301.04525v2-abstract-full').style.display = 'inline'; document.getElementById('2301.04525v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2301.04525v2-abstract-full" style="display: none;"> Age-related macular degeneration (AMD) is the leading cause of blindness in the elderly. Current grading systems based on imaging biomarkers only coarsely group disease stages into broad categories and are unable to predict future disease progression. It is widely believed that this is due to their focus on a single point in time, disregarding the dynamic nature of the disease. In this work, we present the first method to automatically discover biomarkers that capture temporal dynamics of disease progression. Our method represents patient time series as trajectories in a latent feature space built with contrastive learning. Then, individual trajectories are partitioned into atomic sub-sequences that encode transitions between disease states. These are clustered using a newly introduced distance metric. In quantitative experiments we found our method yields temporal biomarkers that are predictive of conversion to late AMD. Furthermore, these clusters were highly interpretable to ophthalmologists who confirmed that many of the clusters represent dynamics that have previously been linked to the progression of AMD, even though they are currently not included in any clinical grading system. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2301.04525v2-abstract-full').style.display = 'none'; document.getElementById('2301.04525v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 20 March, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 11 January, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Submitted to MICCAI2023</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2210.16053">arXiv:2210.16053</a> <span> [<a href="https://arxiv.org/pdf/2210.16053">pdf</a>, <a href="https://arxiv.org/format/2210.16053">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> Automated analysis of diabetic retinopathy using vessel segmentation maps as inductive bias </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Kreitner%2C+L">Linus Kreitner</a>, <a href="/search/eess?searchtype=author&query=Ezhov%2C+I">Ivan Ezhov</a>, <a href="/search/eess?searchtype=author&query=Rueckert%2C+D">Daniel Rueckert</a>, <a href="/search/eess?searchtype=author&query=Paetzold%2C+J+C">Johannes C. Paetzold</a>, <a href="/search/eess?searchtype=author&query=Menten%2C+M+J">Martin J. Menten</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2210.16053v3-abstract-short" style="display: inline;"> Recent studies suggest that early stages of diabetic retinopathy (DR) can be diagnosed by monitoring vascular changes in the deep vascular complex. In this work, we investigate a novel method for automated DR grading based on optical coherence tomography angiography (OCTA) images. Our work combines OCTA scans with their vessel segmentations, which then serve as inputs to task specific networks for… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2210.16053v3-abstract-full').style.display = 'inline'; document.getElementById('2210.16053v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2210.16053v3-abstract-full" style="display: none;"> Recent studies suggest that early stages of diabetic retinopathy (DR) can be diagnosed by monitoring vascular changes in the deep vascular complex. In this work, we investigate a novel method for automated DR grading based on optical coherence tomography angiography (OCTA) images. Our work combines OCTA scans with their vessel segmentations, which then serve as inputs to task specific networks for lesion segmentation, image quality assessment and DR grading. For this, we generate synthetic OCTA images to train a segmentation network that can be directly applied on real OCTA data. We test our approach on MICCAI 2022's DR analysis challenge (DRAC). In our experiments, the proposed method performs equally well as the baseline model. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2210.16053v3-abstract-full').style.display = 'none'; document.getElementById('2210.16053v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 21 December, 2022; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 28 October, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2022. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Submission for MICCAI 2022 Diabetic Retinopathy Analysis Challenge (DRAC) Proceedings, DOI: 10.5281/zenodo.6362349</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2207.11102">arXiv:2207.11102</a> <span> [<a href="https://arxiv.org/pdf/2207.11102">pdf</a>, <a href="https://arxiv.org/format/2207.11102">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> Physiology-based simulation of the retinal vasculature enables annotation-free segmentation of OCT angiographs </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Menten%2C+M+J">Martin J. Menten</a>, <a href="/search/eess?searchtype=author&query=Paetzold%2C+J+C">Johannes C. Paetzold</a>, <a href="/search/eess?searchtype=author&query=Dima%2C+A">Alina Dima</a>, <a href="/search/eess?searchtype=author&query=Menze%2C+B+H">Bjoern H. Menze</a>, <a href="/search/eess?searchtype=author&query=Knier%2C+B">Benjamin Knier</a>, <a href="/search/eess?searchtype=author&query=Rueckert%2C+D">Daniel Rueckert</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2207.11102v1-abstract-short" style="display: inline;"> Optical coherence tomography angiography (OCTA) can non-invasively image the eye's circulatory system. In order to reliably characterize the retinal vasculature, there is a need to automatically extract quantitative metrics from these images. The calculation of such biomarkers requires a precise semantic segmentation of the blood vessels. However, deep-learning-based methods for segmentation mostl… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2207.11102v1-abstract-full').style.display = 'inline'; document.getElementById('2207.11102v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2207.11102v1-abstract-full" style="display: none;"> Optical coherence tomography angiography (OCTA) can non-invasively image the eye's circulatory system. In order to reliably characterize the retinal vasculature, there is a need to automatically extract quantitative metrics from these images. The calculation of such biomarkers requires a precise semantic segmentation of the blood vessels. However, deep-learning-based methods for segmentation mostly rely on supervised training with voxel-level annotations, which are costly to obtain. In this work, we present a pipeline to synthesize large amounts of realistic OCTA images with intrinsically matching ground truth labels; thereby obviating the need for manual annotation of training data. Our proposed method is based on two novel components: 1) a physiology-based simulation that models the various retinal vascular plexuses and 2) a suite of physics-based image augmentations that emulate the OCTA image acquisition process including typical artifacts. In extensive benchmarking experiments, we demonstrate the utility of our synthetic data by successfully training retinal vessel segmentation algorithms. Encouraged by our method's competitive quantitative and superior qualitative performance, we believe that it constitutes a versatile tool to advance the quantitative analysis of OCTA images. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2207.11102v1-abstract-full').style.display = 'none'; document.getElementById('2207.11102v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 22 July, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2022. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted at MICCAI 2022</span> </p> </li> </ol> <div class="is-hidden-tablet">  <span class="help" style="display: inline-block;"><a href="https://github.com/arXiv/arxiv-search/releases">Search v0.5.6 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