Blood vessel segmentation in en-face OCTA images: a frequency based method

• URL: http://arxiv.org/abs/2109.06116v1
• Date: Mon, 13 Sep 2021 16:42:58 GMT
• Title: Blood vessel segmentation in en-face OCTA images: a frequency based method
• Authors: Anna Breger, Felix Goldbach, Bianca S. Gerendas, Ursula Schmidt-Erfurth, Martin Ehler
• Abstract summary: We present a novel method for the vessel identification based on frequency representations of the image. The algorithm is evaluated on an OCTA image data set from $10$ eyes acquired by a Cirrus HD- OCT device.
• Score: 3.6055028453181013
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Optical coherence tomography angiography (OCTA) is a novel noninvasive imaging modality for visualization of retinal blood flow in the human retina. Using specific OCTA imaging biomarkers for the identification of pathologies, automated image segmentations of the blood vessels can improve subsequent analysis and diagnosis. We present a novel method for the vessel identification based on frequency representations of the image, in particular, using so-called Gabor filter banks. The algorithm is evaluated on an OCTA image data set from $10$ eyes acquired by a Cirrus HD-OCT device. The segmentation outcomes received very good qualitative visual evaluation feedback and coincide well with device-specific values concerning vessel density. Concerning locality our segmentations are even more reliable and accurate. Therefore, we suggest the computation of adaptive local vessel density maps that allow straightforward analysis of retinal blood flow.

Related papers

• Synthetic optical coherence tomography angiographs for detailed retinal vessel segmentation without human annotations [12.571349114534597]
  We present a lightweight simulation of the retinal vascular network based on space colonization for faster and more realistic OCTA synthesis. We demonstrate the superior segmentation performance of our approach in extensive quantitative and qualitative experiments on three public datasets.
  arXiv  Detail & Related papers  (2023-06-19T14:01:47Z)
• Affinity Feature Strengthening for Accurate, Complete and Robust Vessel Segmentation [48.638327652506284]
  Vessel segmentation is crucial in many medical image applications, such as detecting coronary stenoses, retinal vessel diseases and brain aneurysms. We present a novel approach, the affinity feature strengthening network (AFN), which jointly models geometry and refines pixel-wise segmentation features using a contrast-insensitive, multiscale affinity approach.
  arXiv  Detail & Related papers  (2022-11-12T05:39:17Z)
• Physiology-based simulation of the retinal vasculature enables annotation-free segmentation of OCT angiographs [8.596819713822477]
  We present a pipeline to synthesize large amounts of realistic OCTA images with intrinsically matching ground truth labels. Our proposed method is based on two novel components: 1) a physiology-based simulation that models the various retinal plexuses and 2) a suite of physics-based image augmentations.
  arXiv  Detail & Related papers  (2022-07-22T14:22:22Z)
• Preservation of High Frequency Content for Deep Learning-Based Medical Image Classification [74.84221280249876]
  An efficient analysis of large amounts of chest radiographs can aid physicians and radiologists. We propose a novel Discrete Wavelet Transform (DWT)-based method for the efficient identification and encoding of visual information.
  arXiv  Detail & Related papers  (2022-05-08T15:29:54Z)
• Parametric Scaling of Preprocessing assisted U-net Architecture for Improvised Retinal Vessel Segmentation [1.3869502085838448]
  We present an image enhancement technique based on the morphological preprocessing coupled with a scaled U-net architecture. A significant improvement as compared to the other algorithms in the domain, in terms of the area under ROC curve (>0.9762) and classification accuracy (>95.47%) are evident from the results.
  arXiv  Detail & Related papers  (2022-03-18T15:26:05Z)
• Symmetry-Enhanced Attention Network for Acute Ischemic Infarct Segmentation with Non-Contrast CT Images [50.55978219682419]
  We propose a symmetry enhanced attention network (SEAN) for acute ischemic infarct segmentation. Our proposed network automatically transforms an input CT image into the standard space where the brain tissue is bilaterally symmetric. The proposed SEAN outperforms some symmetry-based state-of-the-art methods in terms of both dice coefficient and infarct localization.
  arXiv  Detail & Related papers  (2021-10-11T07:13:26Z)
• Explaining Clinical Decision Support Systems in Medical Imaging using Cycle-Consistent Activation Maximization [112.2628296775395]
  Clinical decision support using deep neural networks has become a topic of steadily growing interest. clinicians are often hesitant to adopt the technology because its underlying decision-making process is considered to be intransparent and difficult to comprehend. We propose a novel decision explanation scheme based on CycleGAN activation which generates high-quality visualizations of classifier decisions even in smaller data sets.
  arXiv  Detail & Related papers  (2020-10-09T14:39:27Z)
• Multi-Task Neural Networks with Spatial Activation for Retinal Vessel Segmentation and Artery/Vein Classification [49.64863177155927]
  We propose a multi-task deep neural network with spatial activation mechanism to segment full retinal vessel, artery and vein simultaneously. The proposed network achieves pixel-wise accuracy of 95.70% for vessel segmentation, and A/V classification accuracy of 94.50%, which is the state-of-the-art performance for both tasks.
  arXiv  Detail & Related papers  (2020-07-18T05:46:47Z)
• ROSE: A Retinal OCT-Angiography Vessel Segmentation Dataset and New Model [41.444917622855606]
  We release a dedicated OCT-A SEgmentation dataset (ROSE), which consists of 229 OCT-A images with vessel annotations at either centerline-level or pixel level. Secondly, we propose a novel Split-based Coarse-to-Fine vessel segmentation network (SCF-Net), with the ability to detect thick and thin vessels separately. In the SCF-Net, a split-based coarse segmentation (SCS) module is first introduced to produce a preliminary confidence map of vessels, and a split-based refinement (SRN) module is then used to optimize the shape/contour of
  arXiv  Detail & Related papers  (2020-07-10T06:54:19Z)
• Joint Learning of Vessel Segmentation and Artery/Vein Classification with Post-processing [27.825969553813092]
  Vessel segmentation and artery/vein classification provide various information on potential disorders. We adopt a UNet-based model, SeqNet, to accurately segment vessels from the background and make prediction on the vessel type. Our experiments show that our method improves AUC to 0.98 for segmentation and the accuracy to 0.92 in classification over DRIVE dataset.
  arXiv  Detail & Related papers  (2020-05-27T13:06:16Z)
• Modeling and Enhancing Low-quality Retinal Fundus Images [167.02325845822276]
  Low-quality fundus images increase uncertainty in clinical observation and lead to the risk of misdiagnosis. We propose a clinically oriented fundus enhancement network (cofe-Net) to suppress global degradation factors. Experiments on both synthetic and real images demonstrate that our algorithm effectively corrects low-quality fundus images without losing retinal details.
  arXiv  Detail & Related papers  (2020-05-12T08:01:16Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.