Literature DB >> 26435924

State-of-the-art in retinal optical coherence tomography image analysis.

Ahmadreza Baghaie1, Zeyun Yu1, Roshan M D'Souza1.   

Abstract

Optical coherence tomography (OCT) is an emerging imaging modality that has been widely used in the field of biomedical imaging. In the recent past, it has found uses as a diagnostic tool in dermatology, cardiology, and ophthalmology. In this paper we focus on its applications in the field of ophthalmology and retinal imaging. OCT is able to non-invasively produce cross-sectional volumetric images of the tissues which can be used for analysis of tissue structure and properties. Due to the underlying physics, OCT images suffer from a granular pattern, called speckle noise, which restricts the process of interpretation. This requires specialized noise reduction techniques to eliminate the noise while preserving image details. Another major step in OCT image analysis involves the use of segmentation techniques for distinguishing between different structures, especially in retinal OCT volumes. The outcome of this step is usually thickness maps of different retinal layers which are very useful in study of normal/diseased subjects. Lastly, movements of the tissue under imaging as well as the progression of disease in the tissue affect the quality and the proper interpretation of the acquired images which require the use of different image registration techniques. This paper reviews various techniques that are currently used to process raw image data into a form that can be clearly interpreted by clinicians.

Keywords:  Image analysis; image registration; image segmentation; noise reduction; optical coherence tomography (OCT)

Year:  2015        PMID: 26435924      PMCID: PMC4559975          DOI: 10.3978/j.issn.2223-4292.2015.07.02

Source DB:  PubMed          Journal:  Quant Imaging Med Surg        ISSN: 2223-4306


  62 in total

1.  Retinal thickness measurements from optical coherence tomography using a Markov boundary model.

Authors:  D Koozekanani; K Boyer; C Roberts
Journal:  IEEE Trans Med Imaging       Date:  2001-09       Impact factor: 10.048

2.  Demonstration of dispersion-canceled quantum-optical coherence tomography.

Authors:  Magued B Nasr; Bahaa E A Saleh; Alexander V Sergienko; Malvin C Teich
Journal:  Phys Rev Lett       Date:  2003-08-22       Impact factor: 9.161

3.  Speckle reduction in optical coherence tomography by frequency compounding.

Authors:  Michael Pircher; Erich Gotzinger; Rainer Leitgeb; Adolf F Fercher; Christoph K Hitzenberger
Journal:  J Biomed Opt       Date:  2003-07       Impact factor: 3.170

4.  Robust segmentation of intraretinal layers in the normal human fovea using a novel statistical model based on texture and shape analysis.

Authors:  Vedran Kajić; Boris Povazay; Boris Hermann; Bernd Hofer; David Marshall; Paul L Rosin; Wolfgang Drexler
Journal:  Opt Express       Date:  2010-07-05       Impact factor: 3.894

5.  Quantitative thickness measurement of retinal layers imaged by optical coherence tomography.

Authors:  Mahnaz Shahidi; Zhangwei Wang; Ruth Zelkha
Journal:  Am J Ophthalmol       Date:  2005-06       Impact factor: 5.258

6.  Comparison of PDE-based nonlinear diffusion approaches for image enhancement and denoising in optical coherence tomography.

Authors:  Harry M Salinas; Delia Cabrera Fernández
Journal:  IEEE Trans Med Imaging       Date:  2007-06       Impact factor: 10.048

7.  Interval type-II fuzzy anisotropic diffusion algorithm for speckle noise reduction in optical coherence tomography images.

Authors:  Prabakar Puvanathasan; Kostadinka Bizheva
Journal:  Opt Express       Date:  2009-01-19       Impact factor: 3.894

8.  Speckle in optical coherence tomography.

Authors:  J M Schmitt; S H Xiang; K M Yung
Journal:  J Biomed Opt       Date:  1999-01       Impact factor: 3.170

9.  Array detection for speckle reduction in optical coherence microscopy.

Authors:  J M Schmitt
Journal:  Phys Med Biol       Date:  1997-07       Impact factor: 3.609

10.  Simultaneous SLO/OCT imaging of the human retina with axial eye motion correction.

Authors:  Michael Pircher; Bernhard Baumann; Erich Götzinger; Harald Sattmann; Christoph K Hitzenberger
Journal:  Opt Express       Date:  2007-12-10       Impact factor: 3.894

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  11 in total

1.  Effect of patch size and network architecture on a convolutional neural network approach for automatic segmentation of OCT retinal layers.

Authors:  Jared Hamwood; David Alonso-Caneiro; Scott A Read; Stephen J Vincent; Michael J Collins
Journal:  Biomed Opt Express       Date:  2018-06-11       Impact factor: 3.732

2.  Curvelet Transform-based volume fusion for correcting signal loss artifacts in Time-of-Flight Magnetic Resonance Angiography data.

Authors:  Ahmadreza Baghaie; Susanne Schnell; Ali Bakhshinejad; Mojtaba F Fathi; Roshan M D'Souza; Vitaliy L Rayz
Journal:  Comput Biol Med       Date:  2018-06-15       Impact factor: 4.589

3.  Segmentation guided registration of wide field-of-view retinal optical coherence tomography volumes.

Authors:  José Lezama; Dibyendu Mukherjee; Ryan P McNabb; Guillermo Sapiro; Anthony N Kuo; Sina Farsiu
Journal:  Biomed Opt Express       Date:  2016-11-01       Impact factor: 3.732

Review 4.  Involuntary eye motion correction in retinal optical coherence tomography: Hardware or software solution?

Authors:  Ahmadreza Baghaie; Zeyun Yu; Roshan M D'Souza
Journal:  Med Image Anal       Date:  2017-02-04       Impact factor: 8.545

5.  Real-time OCT image denoising using a self-fusion neural network.

Authors:  Jose J Rico-Jimenez; Dewei Hu; Eric M Tang; Ipek Oguz; Yuankai K Tao
Journal:  Biomed Opt Express       Date:  2022-02-14       Impact factor: 3.732

6.  Application of Independent Component Analysis Techniques in Speckle Noise Reduction of Retinal OCT Images.

Authors:  Ahmadreza Baghaie; Roshan M D'Souza; Zeyun Yu
Journal:  Optik (Stuttg)       Date:  2016-08       Impact factor: 2.443

7.  A Deep Learning Approach to Denoise Optical Coherence Tomography Images of the Optic Nerve Head.

Authors:  Sripad Krishna Devalla; Giridhar Subramanian; Tan Hung Pham; Xiaofei Wang; Shamira Perera; Tin A Tun; Tin Aung; Leopold Schmetterer; Alexandre H Thiéry; Michaël J A Girard
Journal:  Sci Rep       Date:  2019-10-08       Impact factor: 4.379

8.  Retinal Boundary Segmentation in Stargardt Disease Optical Coherence Tomography Images Using Automated Deep Learning.

Authors:  Jason Kugelman; David Alonso-Caneiro; Yi Chen; Sukanya Arunachalam; Di Huang; Natasha Vallis; Michael J Collins; Fred K Chen
Journal:  Transl Vis Sci Technol       Date:  2020-10-13       Impact factor: 3.283

Review 9.  Methodological Challenges of Deep Learning in Optical Coherence Tomography for Retinal Diseases: A Review.

Authors:  Ryan T Yanagihara; Cecilia S Lee; Daniel Shu Wei Ting; Aaron Y Lee
Journal:  Transl Vis Sci Technol       Date:  2020-02-18       Impact factor: 3.048

10.  Automatic choroidal segmentation in OCT images using supervised deep learning methods.

Authors:  Jason Kugelman; David Alonso-Caneiro; Scott A Read; Jared Hamwood; Stephen J Vincent; Fred K Chen; Michael J Collins
Journal:  Sci Rep       Date:  2019-09-16       Impact factor: 4.379

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