scholarly journals Uncovering of intraspecies macular heterogeneity in cynomolgus monkeys using hybrid machine learning optical coherence tomography image segmentation

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Peter M. Maloca ◽  
Christine Seeger ◽  
Helen Booler ◽  
Philippe Valmaggia ◽  
Ken Kawamoto ◽  
...  

AbstractThe fovea is a depression in the center of the macula and is the site of the highest visual acuity. Optical coherence tomography (OCT) has contributed considerably in elucidating the pathologic changes in the fovea and is now being considered as an accompanying imaging method in drug development, such as antivascular endothelial growth factor and its safety profiling. Because animal numbers are limited in preclinical studies and automatized image evaluation tools have not yet been routinely employed, essential reference data describing the morphologic variations in macular thickness in laboratory cynomolgus monkeys are sparse to nonexistent. A hybrid machine learning algorithm was applied for automated OCT image processing and measurements of central retina thickness and surface area values. Morphological variations and the effects of sex and geographical origin were determined. Based on our findings, the fovea parameters are specific to the geographic origin. Despite morphological similarities among cynomolgus monkeys, considerable variations in the foveolar contour, even within the same species but from different geographic origins, were found. The results of the reference database show that not only the entire retinal thickness, but also the macular subfields, should be considered when designing preclinical studies and in the interpretation of foveal data.

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Peter M. Maloca ◽  
Philipp L. Müller ◽  
Aaron Y. Lee ◽  
Adnan Tufail ◽  
Konstantinos Balaskas ◽  
...  

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.


Author(s):  
Simon D. Thackray ◽  
Christos V. Bourantas ◽  
Poay H. Loh ◽  
Vasilios D. Tsakanikas ◽  
Dimitrios I. Fotiadis

Optical coherence tomography (OCT) is a light-based invasive imaging method allowing accurate evaluation of coronary luminal morphology and reliable characterization of plaque. Its high resolution (10-20µm) offers the unique possibility of identifying clinically important coronary plaque microstructures such as macrophages, the presence and type of thrombus, stent expansion and endothelization and provides accurate assessment of the fibrous cap thickness in high risk plaques. These attributes placed OCT in a unique position as useful tool in research and clinical practice. As a new image modality, many interventional cardiologists are not familiar with its interpretation. In addition, there are only few developed methodologies able to process the OCT data and give comprehensive vessel representation and reliable measurements. Thus, this chapter focuses on the interpretation of OCT images and discusses the available image processing methodologies.


2013 ◽  
pp. 513-528
Author(s):  
Simon D. Thackray ◽  
Christos V. Bourantas ◽  
Poay H. Loh ◽  
Vasilios D. Tsakanikas ◽  
Dimitrios I. Fotiadis

Optical coherence tomography (OCT) is a light-based invasive imaging method allowing accurate evaluation of coronary luminal morphology and reliable characterization of plaque. Its high resolution (10-20µm) offers the unique possibility of identifying clinically important coronary plaque microstructures such as macrophages, the presence and type of thrombus, stent expansion and endothelization and provides accurate assessment of the fibrous cap thickness in high risk plaques. These attributes placed OCT in a unique position as useful tool in research and clinical practice. As a new image modality, many interventional cardiologists are not familiar with its interpretation. In addition, there are only few developed methodologies able to process the OCT data and give comprehensive vessel representation and reliable measurements. Thus, this chapter focuses on the interpretation of OCT images and discusses the available image processing methodologies.


2021 ◽  
Vol 127 (4) ◽  
Author(s):  
S. Skruszewicz ◽  
S. Fuchs ◽  
J. J. Abel ◽  
J. Nathanael ◽  
J. Reinhard ◽  
...  

AbstractWe present an overview of recent results on optical coherence tomography with the use of extreme ultraviolet and soft X-ray radiation (XCT). XCT is a cross-sectional imaging method that has emerged as a derivative of optical coherence tomography (OCT). In contrast to OCT, which typically uses near-infrared light, XCT utilizes broad bandwidth extreme ultraviolet (XUV) and soft X-ray (SXR) radiation (Fuchs et al in Sci Rep 6:20658, 2016). As in OCT, XCT’s axial resolution only scales with the coherence length of the light source. Thus, an axial resolution down to the nanometer range can be achieved. This is an improvement of up to three orders of magnitude in comparison to OCT. XCT measures the reflected spectrum in a common-path interferometric setup to retrieve the axial structure of nanometer-sized samples. The technique has been demonstrated with broad bandwidth XUV/SXR radiation from synchrotron facilities and recently with compact laboratory-based laser-driven sources. Axial resolutions down to 2.2 nm have been achieved experimentally. XCT has potential applications in three-dimensional imaging of silicon-based semiconductors, lithography masks, and layered structures like XUV mirrors and solar cells.


2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Yi Sun ◽  
Jianfeng Wang ◽  
Jindou Shi ◽  
Stephen A. Boppart

AbstractPolarization-sensitive optical coherence tomography (PS-OCT) is a high-resolution label-free optical biomedical imaging modality that is sensitive to the microstructural architecture in tissue that gives rise to form birefringence, such as collagen or muscle fibers. To enable polarization sensitivity in an OCT system, however, requires additional hardware and complexity. We developed a deep-learning method to synthesize PS-OCT images by training a generative adversarial network (GAN) on OCT intensity and PS-OCT images. The synthesis accuracy was first evaluated by the structural similarity index (SSIM) between the synthetic and real PS-OCT images. Furthermore, the effectiveness of the computational PS-OCT images was validated by separately training two image classifiers using the real and synthetic PS-OCT images for cancer/normal classification. The similar classification results of the two trained classifiers demonstrate that the predicted PS-OCT images can be potentially used interchangeably in cancer diagnosis applications. In addition, we applied the trained GAN models on OCT images collected from a separate OCT imaging system, and the synthetic PS-OCT images correlate well with the real PS-OCT image collected from the same sample sites using the PS-OCT imaging system. This computational PS-OCT imaging method has the potential to reduce the cost, complexity, and need for hardware-based PS-OCT imaging systems.


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