Analysis of Deep Neural Networks for Detection of Coronary Artery Stenosis

Danilov, Viacheslav V.; Gerget, Olga; Klyshnikov, K. Yu.; Frangi, Alejandro F.; Овчаренко, Е. А.

doi:10.1134/s0361768821030038

Cited by 7 publications

(2 citation statements)

References 14 publications

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“…C-results of C-VUSGAN for segmentation of IVUS images depend not only on the loss function used but also on the network structure of the generator used. Networks such as FCN, U-Net, DeconvNet, and SegNet are classical semantic image segmentation networks that can be used as generators in C-IVUSGAN [ 22 ]. Inspired by the design ideas of stacked hourglass networks (SHGNS)6 and VGG-Net5, this subsection investigates the segmentation effects of three different generators, namely, Pix2ix-1 (U-Ne)67Pix2Pix-2 (FCN)52, and C-IVUSGAN-SHGN.…”

Section: Model Experimental Resultsmentioning

confidence: 99%

Study of Vertebral Artery Dissection by Ultrasound Superb Microvascular Imaging Based on Deep Neural Network Model

Wang

Jiao

Peng

et al. 2022

Journal of Healthcare Engineering

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To assess the diagnostic value of ultrasound Superb Microvascular Imaging (SMI) and versus Doppler ultrasound (TCD) for microvascular structure and aerodynamic changes in vertebral artery dissection (VAD). In this paper, we firstly simulate the process of clinician recognition of vertebral artery dissection and propose a combination of a priori shape information of vertebral artery dissection and deep folly convolutional networks (DFCNs) for IVUS. In this paper, 15 patients with vertebral artery dissection confirmed by SMI, digital subtraction angiography (DSA), or computed tomography angiography (CTA) from 2020 to 2021 were selected, and the true and false lumen diameters, peak systolic flow velocity (PSV), end-diastolic flow velocity (EDV) and PSV, EDV, and plasticity index (PI) of the intracranial vertebral artery were measured. Among the 15 patients with VAD, 4 (27%, 4/15) had trauma-induced secondary vertebral artery entrapment and 11 (73%, 11/15) had spontaneous entrapment without a clear cause. According to the structural characteristics of the vessels, there were 11 cases (73%, 11/15) of double-lumen, intramural hematoma, and vertebral artery dissection aneurysm, and 11 cases (73%, 11/15) of V1 segment. SMI not only provides an objective assessment of the vascular morphology and aerodynamic changes in VAD but also, in combination with TCD, can further determine the opening of the traffic branches in the posterior circulation, providing reliable information for the early diagnosis and treatment of microvascular dissection of the vertebral artery.

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Section: Model Experimental Resultsmentioning

confidence: 99%

Study of Vertebral Artery Dissection by Ultrasound Superb Microvascular Imaging Based on Deep Neural Network Model

Wang

Jiao

Peng

et al. 2022

Journal of Healthcare Engineering

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“…Object detection. Для достижения цели по обна-ружению ключевых точек на кадре было принято решение использовать технологию Object detection, так как она хорошо зарекомендовала себя в ряде других задач [18][19][20][21]. Трекинг, или обнаружение объектов на изображении, является одной из основных задач компьютерного зрения.…”

Section: оригинальные исследованияunclassified

Application of Object Detection Technology in Aortography Keypoint Tracking

Laptev,

Kochergin

2024

Kompleks. probl. serdečno-sosud. zabol.

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HighlightsThe practical significance of the work lies in the fact that the presented aortography data visualization system is an effective tool for visually assisting surgeons during transcatheter aortic valve implantation interventions, supporting real-time operation mode. The proposed data preprocessing algorithm, which improves image quality with minimal performance costs, complements the system, allowing specialists to achieve the best result. AbstractAim. The aim of this study is to develop a visual assistance system for transcatheter aortic valve implantation procedures.Methods. To address the stated objective, our own dataset consisting of 35 videos of the intervention was used. The visualization system is based on the approach of detecting key points in aortography, utilizing “Object detection” technology with the application of YOLO family artificial neural networks. To achieve the best result, we proposed a method to enhance the quality of input data using convolutional neural networks, specifically the «Autoencoder» technology.Results. The results of the study revealed that the convolutional autoencoder model is capable of restoring the informativeness of noisy input images from 40 to 75%, thereby increasing the accuracy of object detection in images. The presented real-time tracking system for facilitating TAVI procedures achieves a final accuracy of 51.9% according to the Mean Average Precision (MAP) quality metric. The visual assistance system can recognize and track key points indicating the location of the aortic root, delivery system, and heart valve prosthesis during surgery. The practical significance of the work lies in the fact that the presented aortography data visualization system is an effective tool for visually assisting surgeons during interventions, supporting real-time operation mode.Conclusion. The proposed data preprocessing algorithm, which improves image quality with minimal performance costs, complements the visualization system, allowing specialists to achieve the best results.

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