Measuring left ventricular volumes in two-dimensional echocardiography image sequence using level-set method for automatic detection of end-diastole and end-systole frames

Darvishi, Saeed; Behnam, Hamid; Pouladian, Majid; Samiei, Niloufar

doi:10.5812/cardiovascmed.6397

Cited by 25 publications

(9 citation statements)

References 33 publications

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“…Whereas ES frames are commonly defined by mitral valve opening, minimal LV volume, aortic valve closure, or the end of the ECG T-wave. However, due to subtle frame-on-frame spatial differences, and complex temporal relationships virtually invisible to the human eye, manual detection presents a significant barrier to consistent diagnosis due to intraand inter-observer variability lacking reproducibility and precision [4].…”

Section: The Need For Fully Automated Systemsmentioning

confidence: 99%

Multibeat echocardiographic phase detection using deep neural networks

Lane

Azarmehr

Jevsikov

et al. 2021

Computers in Biology and Medicine

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Section: The Need For Fully Automated Systemsmentioning

confidence: 99%

Multibeat echocardiographic phase detection using deep neural networks

Lane

Azarmehr

Jevsikov

et al. 2021

Computers in Biology and Medicine

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“…Accurate detection of the End-Distally (ED) and EndSystolic (ES) stages of a cardiac cycle is a significant factor that can affect the accuracy of abnormality assessment of a ventricle (Ostenfeld et al, 2012;Darvishi et al, 2012). This process is a routine step of the ventricle assessment procedures, where often, in clinical reports, many parameters can be measured in these two stages to help in diagnosing and decision making Barcaro et al (2008).…”

Section: End-diastolic and End-systolic Detectionmentioning

confidence: 99%

Cavity Segmentation and Stroke Volume Measurement Methods From Echocardiographic Images: Review

Abboud¹,

Rahmat²,

Kadiman³

et al. 2014

Journal of Computer Science

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The complexity and criticality of heart disease diagnosis and treatment have frequently attracted the attention of researchers to develop approaches that can handle accurate clinical assessments. One of these complexities is measuring the Right Ventricle stroke volume. Therefore, different cardiac imaging techniques have been developed to help specialists in their regular clinical work and in the operating theatre to assist in the diagnosis, monitoring of the treatment and to ensure the success of a cardiac intervention as far as possible. Echocardiography is one of the cardiac imaging techniques and is considered as the most effective imaging technique as it is minimally invasive, quick and the cheapest method. Many studies have been conducted on Right Ventricle assessment from echocariographic images. This review is aimed at presenting the previous technical elements and algorithms used to measure the ventricle stroke volume from echocardiographic images. The review focuses on the five technical approaches that have been used, which are pre-processing, End-Distally (ED) and End-Systolic (ES) frame detection, cavity segmentation, the stoke volume measurement and 3D reconstruction. Finally, a summary will conclude this study.

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“…Several automatic ECG-free detection approaches have been already suggested. Most of them used echo [4,5,6,7,8,9,17] or CMR image sequences [13,19]. Early semi-automatic works [4,6,9] required either manual definition of landmarks, the ED frame or an initial contour.…”

Section: Introductionmentioning

confidence: 99%

Self-supervised motion descriptor for cardiac phase detection in 4D CMR based on discrete vector field estimations

Koehler¹,

Hussain²,

Hussain³

et al. 2022

Preprint

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Cardiac magnetic resonance (CMR) sequences visualise the cardiac function voxel-wise over time. Simultaneously, deep learningbased deformable image registration is able to estimate discrete vector fields which warp one time step of a CMR sequence to the following in a self-supervised manner. However, despite the rich source of information included in these 3D+t vector fields, a standardised interpretation is challenging and the clinical applications remain limited so far. In this work, we show how to efficiently use a deformable vector field to describe the underlying dynamic process of a cardiac cycle in form of a derived 1D motion descriptor. Additionally, based on the expected cardiovascular physiological properties of a contracting or relaxing ventricle, we define a set of rules that enables the identification of five cardiovascular phases including the end-systole (ES) and end-diastole (ED) without usage of labels. We evaluate the plausibility of the motion descriptor on two challenging multi-disease, -center, -scanner short-axis CMR datasets. First, by reporting quantitative measures such as the periodic frame difference for the extracted phases. Second, by comparing qualitatively the general pattern when we temporally resample and align the motion descriptors of all instances across both datasets. The average periodic frame difference for the ED, ES key phases of our approach is 0.80 ± 0.85, 0.69 ± 0.79 which is slightly better than the inter-observer variability (1.07 ± 0.86, 0.91 ± 1.6) and the supervised baseline method (1.18 ± 1.91, 1.21 ± 1.78). Code and labels will be made available on our GitHub repository. https://github.com/Cardio-AI/cmr-phase-detection

show abstract

Measuring left ventricular volumes in two-dimensional echocardiography image sequence using level-set method for automatic detection of end-diastole and end-systole frames

Cited by 25 publications

References 33 publications

Multibeat echocardiographic phase detection using deep neural networks

Multibeat echocardiographic phase detection using deep neural networks

Cavity Segmentation and Stroke Volume Measurement Methods From Echocardiographic Images: Review

Self-supervised motion descriptor for cardiac phase detection in 4D CMR based on discrete vector field estimations

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