HMC-QU echocardiography ultrasound recordings

@article{,
title= {HMC-QU echocardiography ultrasound recordings},
keywords= {ultrasound},
author= {},
abstract= {The HMC-QU benchmark dataset is created by the collaboration between Hamad Medical Corporation (HMC), Tampere University, and Qatar University. The usage of data has been approved by the local ethics board of HMC Hospital in February 2019. The dataset includes a collection of apical 4-chamber (A4C) and apical 2-chamber (A2C) view 2D echocardiography recordings obtained during the years 2018 and 2019. The echocardiography recordings are acquired via devices from different vendors that are Phillips and GE Vivid (GE-Health-USA) ultrasound machines. The temporal resolution (frame rate per second) of the echocardiography recordings is 25 fps. The spatial resolution varies from 422x636 to 768x1024 pixels. The dataset can be utilized for both myocardial infarction (heart attack) detection and left ventricle wall segmentation purposes.

# Detection of Myocardial Infarction

HMC-QU is the first dataset that is shared with the research community serving myocardial infarction (MI) detection on the left ventricle wall of the heart. The recordings are from over 10,000 echos performed in a year including more than 800 cases admitted with acute ST-elevation MI. The patients with MI were treated with coronary angiogram/angioplasty after the diagnosis of acute MI with electrocardiography and cardiac enzymes evidence. The patients had echocardiography recordings obtained within 24 hours of admission or in some cases before they underwent coronary angioplasty. The subjects not diagnosed with MI underwent a required health check and investigation for other reasons in the hospital.

The ground-truth labels are provided for each myocardial segment illustrated in Figure 1 as non-MI and MI, where the MI term indicates any sign of regional wall motion abnormality, whereas the subjects without regional wall motion abnormality are assigned to non-MI. The one cardiac cycle frames are predefined for each recording. End-diastole and end-systole frames are defined according to the electrocardiography (ECG) recordings of the patients. For the patients without ECG recordings, the cardiac cycle is defined according to the frames, where the left ventricle area is the largest and smallest.

1.1. Apical 4-chamber

HMC-QU dataset consists of 162 A4C view 2D echocardiography recordings. The A4C view recordings belong to 93 MI patients (all first-time and acute MI) and 69 non-MI subjects.

1.2. Apical 2-chamber

The dataset consists of 130 A2C view 2D echocardiography recordings that belong to 68 MI patients and 62 non-MI subjects.

# Segmentation of the Left Ventricle Wall

A subset of 109 A4C view echocardiography recordings has their corresponding ground-truth segmentation masks for the whole left ventricle wall at each frame for one cardiac cycle. This subset includes 72 MI patients and 37 non-MI subjects. The size of the ground-truth segmentation masks is 224x224 in order to have suitable input dimensions for many state-of-the-art deep network topologies.

If you use the HMC-QU dataset in your research, please consider citing the publications below:

[P1] A. Degerli, S. Kiranyaz, T. Hamid, R. Mazhar, and M. Gabbouj, “Early Myocardial Infarction Detection over Multi-view Echocardiography,” arXiv preprint arXiv:2111.05790v2, 2021, https://doi.org/10.48550/arXiv.2111.05790.

[P2] A. Degerli, M. Zabihi, S. Kiranyaz, T. Hamid, R. Mazhar, R. Hamila, and M. Gabbouj, "Early Detection of Myocardial Infarction in Low-Quality Echocardiography," in IEEE Access, vol. 9, pp. 34442-34453, 2021, https://doi.org/10.1109/ACCESS.2021.3059595.

[P3] S. Kiranyaz, A. Degerli, T. Hamid, R. Mazhar, R. E. F. Ahmed, R. Abouhasera, M. Zabihi, J. Malik, R. Hamila, and M. Gabbouj, "Left Ventricular Wall Motion Estimation by Active Polynomials for Acute Myocardial Infarction Detection," in IEEE Access, vol. 8, pp. 210301-210317, 2020, https://doi.org/10.1109/ACCESS.2020.3038743.

https://i.imgur.com/QKsdWPb.jpg},
terms= {},
license= {https://creativecommons.org/licenses/by-nc-sa/3.0/igo/},
superseded= {},
url= {https://www.kaggle.com/datasets/aysendegerli/hmcqu-dataset}
}