Trends in optical coherence tomography applied to medical imaging

2014 ◽  
Author(s):  
Adrian G. Podoleanu
Keyword(s):  
Optical Coherence Tomography ◽  
Medical Imaging ◽  
Optical Coherence

Optical coherence tomography among medical imaging modalities: potential and limitations

2007 ◽  
Author(s):  
Natalia Gladkova ◽  
Elena Zagaynova ◽  
Natalia Shakhova ◽  
Alexander M. Sergeev ◽  
Valentin Gelikonov ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Medical Imaging ◽  
Imaging Modalities ◽  
Optical Coherence

scholarly journals Feature definition and comprehensive analysis on the robust identification of intraretinal cystoid regions using optical coherence tomography images

2021 ◽  
Author(s):  
Joaquim de Moura ◽  
Plácido L. Vidal ◽  
Jorge Novo ◽  
José Rouco ◽  
Manuel G. Penedo ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Medical Imaging ◽  
Imaging Modality ◽  
Developed Countries ◽  
Point Of View ◽  
Machine Learning Techniques ◽  
Optical Coherence ◽  
Cross Sectional ◽  
Factors Affecting ◽  
Invasive Techniques

AbstractCurrently, optical coherence tomography is one of the most used medical imaging modalities, offering cross-sectional representations of the studied tissues. This image modality is specially relevant for the analysis of the retina, since it is the internal part of the human body that allows an almost direct examination without invasive techniques. One of the most representative cases of use of this medical imaging modality is for the identification and characterization of intraretinal fluid accumulations, critical for the diagnosis of one of the main causes of blindness in developed countries: the Diabetic Macular Edema. The study of these fluid accumulations is particularly interesting, both from the point of view of pattern recognition and from the different branches of health sciences. As these fluid accumulations are intermingled with retinal tissues, they present numerous variants according to their severity, and change their appearance depending on the configuration of the device; they are a perfect subject for an in-depth research, as they are considered to be a problem without a strict solution. In this work, we propose a comprehensive and detailed analysis of the patterns that characterize them. We employed a pool of 11 different texture and intensity feature families (giving a total of 510 markers) which we have analyzed using three different feature selection strategies and seven complementary classification algorithms. By doing so, we have been able to narrow down and explain the factors affecting this kind of accumulations and tissue lesions by means of machine learning techniques with a pipeline specially designed for this purpose.

A review of optical coherence tomography in breast cancer

2014 ◽  
Vol 3 (3) ◽  
Author(s):  
Loretta Scolaro ◽  
Robert A. McLaughlin ◽  
Brendan F. Kennedy ◽  
Christobel M. Saunders ◽  
David D. Sampson
Keyword(s):  
Breast Cancer ◽  
Optical Coherence Tomography ◽  
Medical Imaging ◽  
Imaging Modality ◽  
Three Dimensional ◽  
Tissue Structure ◽  
Optical Coherence ◽  
Micro Scale ◽  
Bulk Tissue

AbstractOptical coherence tomography (OCT) is a medical imaging modality that opens up new opportunities for imaging in breast cancer. It provides two- and three-dimensional micro-scale images of tissue structure from bulk tissue,

scholarly journals Generating retinal flow maps from structural optical coherence tomography with artificial intelligence

2018 ◽  
Author(s):  
Cecilia S. Lee ◽  
Ariel J. Tyring ◽  
Yue Wu ◽  
Sa Xiao ◽  
Ariel S. Rokem ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Optical Coherence Tomography ◽  
Medical Imaging ◽  
Optical Coherence Tomography Angiography ◽  
Imaging Modality ◽  
Body Part ◽  
Retinal Blood Flow ◽  
Optical Coherence ◽  
Flow Maps ◽  
Better Than

ABSTRACTDespite advances in artificial intelligence (AI), its application in medical imaging has been burdened and limited by expert-generated labels. We used images from optical coherence tomography angiography (OCTA), a relatively new imaging modality that measures retinal blood flow, to train an AI algorithm to generate flow maps from standard optical coherence tomography (OCT) images, exceeding the ability and bypassing the need for expert labeling. Deep learning was able to infer flow from single structural OCT images with similar fidelity to OCTA and significantly better than expert clinicians (P < 0.00001). Our model allows generating flow maps from large volumes of previously collected OCT data in existing clinical trials and clinical practice. This finding demonstrates a novel application of AI to medical imaging, whereby subtle regularities between different modalities are used to image the same body part and AI is used to generate detailed inferences of tissue function from structure imaging.

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