Segmentation of Retinal Low-Cost Optical Coherence Tomography Images using Deep Learning

• URL: http://arxiv.org/abs/2001.08480v1
• Date: Thu, 23 Jan 2020 12:55:53 GMT
• Title: Segmentation of Retinal Low-Cost Optical Coherence Tomography Images using Deep Learning
• Authors: Timo Kepp, Helge Sudkamp, Claus von der Burchard, Hendrik Schenke, Peter Koch, Gereon H\"uttmann, Johann Roider, Mattias P. Heinrich, and Heinz Handels
• Abstract summary: The need for treatment is determined by the presence or change of disease-specific OCT-based biomarkers. The monitoring frequency of current treatment schemes is not individually adapted to the patient and therefore often insufficient. One of the key requirements of a home monitoring OCT system is a computer-aided diagnosis to automatically detect and quantify pathological changes.
• Score: 2.571523045125397
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The treatment of age-related macular degeneration (AMD) requires continuous eye exams using optical coherence tomography (OCT). The need for treatment is determined by the presence or change of disease-specific OCT-based biomarkers. Therefore, the monitoring frequency has a significant influence on the success of AMD therapy. However, the monitoring frequency of current treatment schemes is not individually adapted to the patient and therefore often insufficient. While a higher monitoring frequency would have a positive effect on the success of treatment, in practice it can only be achieved with a home monitoring solution. One of the key requirements of a home monitoring OCT system is a computer-aided diagnosis to automatically detect and quantify pathological changes using specific OCT-based biomarkers. In this paper, for the first time, retinal scans of a novel self-examination low-cost full-field OCT (SELF-OCT) are segmented using a deep learning-based approach. A convolutional neural network (CNN) is utilized to segment the total retina as well as pigment epithelial detachments (PED). It is shown that the CNN-based approach can segment the retina with high accuracy, whereas the segmentation of the PED proves to be challenging. In addition, a convolutional denoising autoencoder (CDAE) refines the CNN prediction, which has previously learned retinal shape information. It is shown that the CDAE refinement can correct segmentation errors caused by artifacts in the OCT image.

Related papers

• Deep learning network to correct axial and coronal eye motion in 3D OCT retinal imaging [65.47834983591957]
  We propose deep learning based neural networks to correct axial and coronal motion artifacts in OCT based on a single scan. The experimental result shows that the proposed method can effectively correct motion artifacts and achieve smaller error than other methods.
  arXiv  Detail & Related papers  (2023-05-27T03:55:19Z)
• Reliable Joint Segmentation of Retinal Edema Lesions in OCT Images [55.83984261827332]
  In this paper, we propose a novel reliable multi-scale wavelet-enhanced transformer network. We develop a novel segmentation backbone that integrates a wavelet-enhanced feature extractor network and a multi-scale transformer module. Our proposed method achieves better segmentation accuracy with a high degree of reliability as compared to other state-of-the-art segmentation approaches.
  arXiv  Detail & Related papers  (2022-12-01T07:32:56Z)
• A Spatiotemporal Model for Precise and Efficient Fully-automatic 3D Motion Correction in OCT [10.550562752812894]
  OCT instruments image by-scanning a focused light spot across the retina, acquiring cross-sectional images to generate data. Patient eye motion during the acquisition poses unique challenges: non-rigid, distorted distortions occur, leading to gaps in data. We present a new distortion model and a corresponding fully-automatic, reference-free optimization strategy for computational robustness.
  arXiv  Detail & Related papers  (2022-09-15T11:48:53Z)
• Automatic Detection of Microaneurysms in OCT Images Using Bag of Features [8.777674946755717]
  Diabetic Retinopathy (DR) caused by diabetes occurs as a result of changes in the retinal vessels and causes visual impairment. Microaneurysms (MAs) are the early clinical signs of DR, whose timely diagnosis can help detecting DR in the early stages of its development. This work is done using the dataset collected from FA and OCT images of 20 patients with DR.
  arXiv  Detail & Related papers  (2022-05-10T06:43:01Z)
• 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)
• Automatic Segmentation of the Optic Nerve Head Region in Optical Coherence Tomography: A Methodological Review [4.777796444711511]
  The optic nerve head represents the intraocular section of the optic nerve (ONH) The advent of optical coherence tomography has enabled the evaluation of novel optic nerve head parameters. Deep learning-based algorithms provide the highest accuracy, sensitivity and specificity for segmenting the different structures of the ONH.
  arXiv  Detail & Related papers  (2021-09-06T09:45:57Z)
• An Interpretable Multiple-Instance Approach for the Detection of referable Diabetic Retinopathy from Fundus Images [72.94446225783697]
  We propose a machine learning system for the detection of referable Diabetic Retinopathy in fundus images. By extracting local information from image patches and combining it efficiently through an attention mechanism, our system is able to achieve high classification accuracy. We evaluate our approach on publicly available retinal image datasets, in which it exhibits near state-of-the-art performance.
  arXiv  Detail & Related papers  (2021-03-02T13:14:15Z)
• Automatic Segmentation of Gross Target Volume of Nasopharynx Cancer using Ensemble of Multiscale Deep Neural Networks with Spatial Attention [2.204996105506197]
  We propose a 2.5D Convolutional Neural Network (CNN) to handle the difference of inplane and through-plane resolution. We also propose a spatial attention module to enable the network to focus on small target, and use channel attention to further improve the segmentation performance.
  arXiv  Detail & Related papers  (2021-01-27T08:20:49Z)
• Assignment Flow for Order-Constrained OCT Segmentation [0.0]
  The identification of retinal layer thicknesses serves as an essential task be done for each patient separately. The elaboration of automated segmentation models has become an important task in the field of medical image processing. We propose a novel, purely data driven textitgeometric approach to order-constrained 3D OCT retinal cell layer segmentation
  arXiv  Detail & Related papers  (2020-09-10T01:57:53Z)
• Spectral-Spatial Recurrent-Convolutional Networks for In-Vivo Hyperspectral Tumor Type Classification [49.32653090178743]
  We demonstrate the feasibility of in-vivo tumor type classification using hyperspectral imaging and deep learning. Our best model achieves an AUC of 76.3%, significantly outperforming previous conventional and deep learning methods.
  arXiv  Detail & Related papers  (2020-07-02T12:00:53Z)
• Retinopathy of Prematurity Stage Diagnosis Using Object Segmentation and Convolutional Neural Networks [68.96150598294072]
  Retinopathy of Prematurity (ROP) is an eye disorder primarily affecting premature infants with lower weights. It causes proliferation of vessels in the retina and could result in vision loss and, eventually, retinal detachment, leading to blindness. In recent years, there has been a significant effort to automate the diagnosis using deep learning. This paper builds upon the success of previous models and develops a novel architecture, which combines object segmentation and convolutional neural networks (CNN) Our proposed system first trains an object segmentation model to identify the demarcation line at a pixel level and adds the resulting mask as an additional "color" channel in
  arXiv  Detail & Related papers  (2020-04-03T14:07:41Z)

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.