Shape prior generation and geodesic active contour interactive iterating algorithm (SPACIAL): fully automatic segmentation for 3D lumen in intravascular optical coherence tomography images

2021 ◽  
Author(s):  
Luying Gui ◽  
Jun Ma ◽  
Xiaoping Yang
2016 ◽  
Author(s):  
Giovanni Jacopo J. Ughi ◽  
Michalina J. Gora ◽  
Anne-Fre Swager ◽  
Mireille Rosenberg ◽  
Jenny Sauk ◽  
...  

2021 ◽  
Vol 11 (12) ◽  
pp. 5488
Author(s):  
Wei Ping Hsia ◽  
Siu Lun Tse ◽  
Chia Jen Chang ◽  
Yu Len Huang

The purpose of this article is to evaluate the accuracy of the optical coherence tomography (OCT) measurement of choroidal thickness in healthy eyes using a deep-learning method with the Mask R-CNN model. Thirty EDI-OCT of thirty patients were enrolled. A mask region-based convolutional neural network (Mask R-CNN) model composed of deep residual network (ResNet) and feature pyramid networks (FPNs) with standard convolution and fully connected heads for mask and box prediction, respectively, was used to automatically depict the choroid layer. The average choroidal thickness and subfoveal choroidal thickness were measured. The results of this study showed that ResNet 50 layers deep (R50) model and ResNet 101 layers deep (R101). R101 U R50 (OR model) demonstrated the best accuracy with an average error of 4.85 pixels and 4.86 pixels, respectively. The R101 ∩ R50 (AND model) took the least time with an average execution time of 4.6 s. Mask-RCNN models showed a good prediction rate of choroidal layer with accuracy rates of 90% and 89.9% for average choroidal thickness and average subfoveal choroidal thickness, respectively. In conclusion, the deep-learning method using the Mask-RCNN model provides a faster and accurate measurement of choroidal thickness. Comparing with manual delineation, it provides better effectiveness, which is feasible for clinical application and larger scale of research on choroid.


2021 ◽  
pp. 159101992110034
Author(s):  
Andre Monteiro ◽  
Demetrius K Lopes ◽  
Amin Aghaebrahim ◽  
Ricardo Hanel

Purpose Flow-diverters have revolutionized the endovascular treatment of intracranial aneurysms, offering a durable solution to aneurysms with high recurrence rates after conventional stent-assisted coiling. Events that occur after treatment with flow-diversion, such as in-stent stenosis (ISS) are not well understood and require further assessment. After assessing an animal model with Optical Coherence Tomography (OCT), we propose a concept that could explain the mechanism causing reversible ISS after treatment of intracranial aneurysms with flow-diverters. Methods Six Pipeline Flex embolization devices (PED-Flex), six PED with Shield technology (PED-Shield), and four Solitaire AB devices were implanted in the carotid arteries (two stents per vessel) of four pigs. Intravascular optical coherence tomography (OCT) and digital subtraction angiography (DSA) images obtained on day 21 were compared to histological specimens. Results A case of ISS in a PED-Flex device was assessed with OCT imaging. Neointima with asymmetrical topography completely covering the PED struts was observed. Histological preparations of the stenotic area demonstrated thrombus on the surface of device struts, covered by neointima. Conclusion This study provides a plausible concept for reversible ISS in flow-diverters. Based on an observation of a previous experiment, we propose that similar cases of ISS are related to thrombus presence underneath endothelization, but further experiments focused on this phenomenon are needed. Optical Coherence Tomography will be useful tool when available for clinical use.


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