Evaluation of potential image acquisition pitfalls during optical coherence tomography and their influence on retinal image segmentation

2007 ◽  
Vol 12 (4) ◽  
pp. 041209 ◽  
Author(s):  
Gábor Márk Somfai ◽  
Harry M. Salinas ◽  
Carmen A. Puliafito ◽  
Delia Cabrera Fernández
Keyword(s):  
Optical Coherence Tomography ◽  
Image Segmentation ◽  
Retinal Image ◽  
Image Acquisition ◽  
Optical Coherence ◽  
Retinal Image Segmentation

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.

A Study on Retinal Image Segmentation and Registration Methods

2021 ◽  
pp. 25-33
Author(s):  
B. Sivaranjani ◽  
C. Kalaiselvi
Keyword(s):  
Image Segmentation ◽  
Retinal Image ◽  
Template Matching ◽  
Diagnosis And Treatment ◽  
Retinal Images ◽  
Fundus Images ◽  
Image Preprocessing ◽  
Fundus Camera ◽  
Motion Parameters ◽  
Retinal Image Segmentation

Diagnosis and treatment of several disorders affecting the retina and the choroid behind it require capturing a sequence of fundus images using the fundus camera. These images are to be processed for better diagnosis and planning of treatment. Retinal image template matching is greatly required to extract certain features that may help in diagnosis and treatment. Also registration of retinal images is very useful in extracting the motion parameters that help in composing a complete map for the retina as well as in retinal tracking. This paper introduces a survey for the image preprocessing, dimensionality reduction, template matching and registration techniques that were reported as being well for retinal images.

scholarly journals Deep Retinal Image Segmentation With Regularization Under Geometric Priors

2020 ◽  
Vol 29 ◽  
pp. 2552-2567 ◽  
Author(s):  
Venkateswararao Cherukuri ◽  
Vijay Kumar B.G. ◽  
Raja Bala ◽  
Vishal Monga
Keyword(s):  
Image Segmentation ◽  
Retinal Image ◽  
Retinal Image Segmentation
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