Fast optical coherence tomography image enhancement using deep learning for smart laser surgery: preliminary study in bone tissue

2019 ◽  
Author(s):  
Yakub Aqib Bayhaqi ◽  
Georg Router ◽  
Alexander Navarini ◽  
Philippe Claude Cattin ◽  
Azhar Zam
Keyword(s):  
Optical Coherence Tomography ◽  
Deep Learning ◽  
Image Enhancement ◽  
Bone Tissue ◽  
Laser Surgery ◽  
Optical Coherence Tomography Image ◽  
Optical Coherence ◽  
Tomography Image ◽  
Preliminary Study

scholarly journals Retinal optical coherence tomography image enhancement via deep learning

2018 ◽  
Vol 9 (12) ◽  
pp. 6205 ◽  
Author(s):  
Kerry J. Halupka ◽  
Bhavna J. Antony ◽  
Matthew H. Lee ◽  
Katie A. Lucy ◽  
Ravneet S. Rai ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Deep Learning ◽  
Image Enhancement ◽  
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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