Neural Network-based Arterial Diameter Estimation from Ultrasound Data

Zhuangzhuang Yu, Manolis Sifalakis, Borbála Hunyadi, Fabian Beutel

Abstract

Cardiovascular diseases are the leading cause of mortality and early assessment of carotid artery abnormalities with ultrasound is key for effective prevention. Obtaining the carotid diameter waveform is essential for hemodynamic parameter extraction. However, since it is not a trivial task to automate, compact computational models are needed to operate reliably in view of physiological variability. Modern machine learning (ML) techniques hold promise for fully automated carotid diameter extraction from ultrasonic data without requiring annotation by trained clinicians. 

Using a conventional digital signal processing (DSP) based approach as reference, our goal is to (a) build data-driven ML models to identify and track the carotid diameter, and (b) keep the computational complexity minimal for deployment in embedded systems. A ML pipeline is developed to estimate the carotid artery diameter from Hilbert-transformed ultrasound signals acquired at 500Hz sampling frequency.

Introduction
Background
Cardiovascular Diseases (CVDs) are the leading cause of premature human mortality globally [1]. CVDs are posing an insidious danger as they typically evolve asymptomatically before having lethal consequences like strokes and heart attacks, or leading to costly chronic conditions like kidney disease and neural impairments from brain damage [2]. Various interventions to assess CVD risk involve the assessment and monitoring of the carotid artery health by means of ultrasonography. Besides conventional hemodynamics like arterial blood flow (e.g., to detect stenosis), arterial stiffness based on pulse wave velocity measurements is becoming increasingly relevant as biomarker in CVD diagnostics and prevention as it reflects the vascular aging and individual susceptibility to hypertension-mediated organ damage

Methods

The proposed solution is a neural-network based processing pipeline that consists of an ROI-Detection module, a tracking module, and a post-processing smoothing module. Each neural network is trained with a small dataset created for this purpose from recordings in a clinical setting. As a starting point and baseline for comparison for the work in this paper, we used a DSP-based approach as reference [17]. In the following sections we outline the data acquisition setup and dataset creation process, and the design of the individual neural networks

Results

The result section is divided into the ROI detector network and the diameter tracking network. Firstly, Fig 6 illustrates the qualitative performance of the ROI detector network by providing a typical example. Fig 6(A) is the M-mode signal that contains the arterial walls near sample depth 300 and 600 as well as other artefacts around sample depth 200, 650, 900 and 1000. Fig 6(B) shows in M-mode only the response vector labels (white region) and the lumen center (blue line). Fig 6(C) shows in M-mode the output of the ROI detector before the argmax operation, depicting as probabilities (intensity) the likely location of the lumen center. 

Discussion

This work proposes a system for automated tracking of the artery diameter consisting of a cascade of two neural networks (NNs): the first network implementing a hard-attention model for a ROI detection that isolates the region of the wall positions, feeding into the second network responsible for the diameter tracking. Attention models (soft-attention in discrete language applications and hard-attention in image processing) are becoming commonplace in deep learning literature for dealing with the computational efficiency and/or robustness of very high dimensional inputs. In most cases one seeks to train the attention mechanism end-to-end or in tandem with the downstream image processing model. 

Conclusion

This work proposed and evaluated a machine learning neural-network based pipeline to detect and track the carotid artery diameter from an ultrasound stream of A-mode frames. Our evaluation showed that the proposed solution results in only 0.6–1.4% deviation in the tracking of the carotid diameter by comparison to the reference, coming from a DSP-based solution, with R2 = 0.7243.

Citation: Yu Z, Sifalakis M, Hunyadi B, Beutel F (2024) Neural network-based arterial diameter estimation from ultrasound data. PLOS Digit Health 3(12): e0000659. https://doi.org/10.1371/journal.pdig.0000659

Editor: Rabie Adel El Arab, Almoosa College of Health Sciences, SAUDI ARABIA
Received: December 12, 2023; Accepted: October 4, 2024; Published: December 2, 2024

Copyright: © 2024 Yu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The data is owned by IMEC and the authors do not have permission to share the data publicly as we are bound to the European General Data Protection Regulation (GDPR) as well as the subjects' consent, stating that data may only be used for specific purposes and not be shared with 3rd parties. This is because the dataset comprises personal identifiable data, which not only holds for demographics but also applies to electrocardiogram or ultrasonic arterial waveforms. That being clarified, there may be ways to make anonymized or minimized data available on requests. However, this must be governed by a data sharing and/or processing agreement, which limits the use of the data (e.g. only to consented purpose, with no attempts to re-identify subjects, etc.). The data is owned by: Stichting imec Nederland High Tech Campus 31 5656 AE Eindhoven The Netherlands. In first instance, please contact privacy@imec.nl.

Funding: The author(s) received no specific funding for this work.

Competing Interests: The authors have declared that no competing interests exist.

 

 

Source: https://journals.plos.org/digitalhealth/article?id=10.1371/journal.pdig.0000659