Reconstruction of tissue birefringence from polarization-sensitive optical coherence tomography using machine learning

2021 ◽  
Author(s):  
Ludovick Bégin ◽  
Damon T. DePaoli ◽  
Brett E. Bouma ◽  
Daniel Côté ◽  
Martin Villiger
Keyword(s):  
Machine Learning ◽  
Optical Coherence Tomography ◽  
Optical Coherence

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.

scholarly journals Optical Coherence Tomography Machine Learning Classifiers for Glaucoma Detection: A Preliminary Study

2005 ◽  
Vol 46 (11) ◽  
pp. 4147 ◽  
Author(s):  
Zvia Burgansky-Eliash ◽  
Gadi Wollstein ◽  
Tianjiao Chu ◽  
Joseph D. Ramsey ◽  
Clark Glymour ◽  
...  
Keyword(s):  
Machine Learning ◽  
Optical Coherence Tomography ◽  
Optical Coherence ◽  
Machine Learning Classifiers ◽  
Learning Classifiers ◽  
Glaucoma Detection ◽  
Preliminary Study

scholarly journals Expert-level Automated Biomarker Identification in Optical Coherence Tomography Scans

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Thomas Kurmann ◽  
Siqing Yu ◽  
Pablo Márquez-Neila ◽  
Andreas Ebneter ◽  
Martin Zinkernagel ◽  
...  
Keyword(s):  
Machine Learning ◽  
Optical Coherence Tomography ◽  
Critical Role ◽  
Cost Effective ◽  
Automated Analysis ◽  
Machine Learning Algorithms ◽  
Three Dimensions ◽  
Optical Coherence ◽  
Clinical Routine ◽  
Wide Range

Abstract In ophthalmology, retinal biological markers, or biomarkers, play a critical role in the management of chronic eye conditions and in the development of new therapeutics. While many imaging technologies used today can visualize these, Optical Coherence Tomography (OCT) is often the tool of choice due to its ability to image retinal structures in three dimensions at micrometer resolution. But with widespread use in clinical routine, and growing prevalence in chronic retinal conditions, the quantity of scans acquired worldwide is surpassing the capacity of retinal specialists to inspect these in meaningful ways. Instead, automated analysis of scans using machine learning algorithms provide a cost effective and reliable alternative to assist ophthalmologists in clinical routine and research. We present a machine learning method capable of consistently identifying a wide range of common retinal biomarkers from OCT scans. Our approach avoids the need for costly segmentation annotations and allows scans to be characterized by biomarker distributions. These can then be used to classify scans based on their underlying pathology in a device-independent way.

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