Машинное обучение в задаче детекции элементов корня аорты

Abstract

A significant increase in the number of transcatheter aortic valve replacements entails the development of auxiliary systems that solve the problem of intra- or preoperative assistance to such interventions. The main concept of such systems is the concept of computerized automatic anatomical recognition of the main landmarks that are key to the procedure. In the case of transcatheter prosthetics – elements of the aortic root and delivery system. This work is aimed at demonstrating the potential of using machine learning methods, the modern architecture of the ResNet V2 convolutional neural network, for the task of intraoperative real-time tracking of the main anatomical landmarks during transcatheter aortic valve replacement. The basis for training the chosen architecture of the neural network was the clinical graphical data of 5 patients who received transcatheter aortic valve replacement using commercial CoreValve systems (Medtronic Inc., USA). The intraoperative aortographs obtained during such an intervention with visualization of the main anatomical landmarks: elements of the fibrous ring of the aortic valve, sinotubular articulation and elements of the delivery system, became the output data for the work of the selected neural network. The total number of images was 2000, which were randomly distributed into two subsamples: 1400 images for training; 600 – for validation. It is shown that the used architecture of the neural network is capable of performing detection with an accuracy of 95-96% in terms of the metrics of the classification and localization components, however, to a large extent does not meet the performance requirements (processing speed): the processing time for one aortography frame was 0.097 sec. The results obtained determine the further direction of development of automatic anatomical recognition of the main landmarks in transcatheter aortic valve replacement from the standpoint of creating an assisting system – reducing the time of analysis of each frame due to the optimization methods described in the literature.