Influence of Pseudophakic Lens Capsule Opacification on Spectral Domain and Time Domain Optical Coherence Tomography Image Quality

2014 ◽  
Vol 40 (6) ◽  
pp. 579-584 ◽  
Author(s):  
Carlo Cagini ◽  
Francesca Pietrolucci ◽  
Marco Lupidi ◽  
Marco Messina ◽  
Francesco Piccinelli ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Image Quality ◽  
Time Domain ◽  
Lens Capsule ◽  
Spectral Domain ◽  
Optical Coherence Tomography Image ◽  
Optical Coherence ◽  
Tomography Image

SPECTRAL DOMAIN-OPTICAL COHERENCE TOMOGRAPHY IMAGE CONTRAST AND BACKGROUND COLOR SETTINGS INFLUENCE IDENTIFICATION OF RETINAL STRUCTURES

Retina ◽  
2016 ◽  
Vol 36 (10) ◽  
pp. 1888-1896 ◽  
Author(s):  
Camille V. Palma ◽  
Ruchita Amin ◽  
Wolfgang Huf ◽  
Ferdinand Schlanitz ◽  
Katharina Eibenberger ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Image Contrast ◽  
Spectral Domain ◽  
Optical Coherence Tomography Image ◽  
Optical Coherence ◽  
Background Color ◽  
Tomography Image

scholarly journals Morphometric parameters of the optic disc in normal and glaucomatous eyes based on time-domain optical coherence tomography image analysis

Medicina ◽  
2017 ◽  
Vol 53 (4) ◽  
pp. 242-252
Author(s):  
Dovilė Buteikienė ◽  
Asta Kybartaitė-Žilienė ◽  
Loresa Kriaučiūnienė ◽  
Valerijus Barzdžiukas ◽  
Ingrida Janulevičienė ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Image Analysis ◽  
Optic Disc ◽  
Time Domain ◽  
Morphometric Parameters ◽  
Optical Coherence Tomography Image ◽  
Optical Coherence ◽  
Tomography Image

scholarly journals Unraveling the deep learning gearbox in optical coherence tomography image segmentation towards explainable artificial intelligence

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Peter M. Maloca ◽  
Philipp L. Müller ◽  
Aaron Y. Lee ◽  
Adnan Tufail ◽  
Konstantinos Balaskas ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Optical Coherence Tomography ◽  
Image Segmentation ◽  
Convolutional Neural Network ◽  
Learning Algorithm ◽  
Ground Truth ◽  
Optical Coherence Tomography Image ◽  
Optical Coherence ◽  
Tomography Image

AbstractMachine learning has greatly facilitated the analysis of medical data, while the internal operations usually remain intransparent. To better comprehend these opaque procedures, a convolutional neural network for optical coherence tomography image segmentation was enhanced with a Traceable Relevance Explainability (T-REX) technique. The proposed application was based on three components: ground truth generation by multiple graders, calculation of Hamming distances among graders and the machine learning algorithm, as well as a smart data visualization (‘neural recording’). An overall average variability of 1.75% between the human graders and the algorithm was found, slightly minor to 2.02% among human graders. The ambiguity in ground truth had noteworthy impact on machine learning results, which could be visualized. The convolutional neural network balanced between graders and allowed for modifiable predictions dependent on the compartment. Using the proposed T-REX setup, machine learning processes could be rendered more transparent and understandable, possibly leading to optimized applications.

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