CAMUS Cardiac Acquisitions for Multi-structure Ultrasound Segmentation

CAMUS_database_nifti (7500 files)
patient0186/MANDATORY_CITATION.md 0.33kB
patient0186/Info_2CH.cfg 0.07kB
patient0186/Info_4CH.cfg 0.07kB
patient0185/patient0185_4CH_half_sequence_gt.nii.gz 80.72kB
patient0185/patient0185_4CH_half_sequence.nii.gz 3.70MB
patient0185/patient0185_4CH_ES_gt.nii.gz 4.05kB
patient0185/patient0185_4CH_ES.nii.gz 191.01kB
patient0185/patient0185_4CH_ED_gt.nii.gz 4.59kB
patient0185/patient0185_4CH_ED.nii.gz 193.71kB
patient0185/patient0185_2CH_half_sequence_gt.nii.gz 39.40kB
patient0185/patient0185_2CH_half_sequence.nii.gz 2.14MB
patient0185/patient0185_2CH_ES_gt.nii.gz 3.60kB
patient0185/patient0185_2CH_ES.nii.gz 194.15kB
patient0185/patient0185_2CH_ED_gt.nii.gz 3.96kB
patient0185/patient0185_2CH_ED.nii.gz 193.58kB
patient0185/MANDATORY_CITATION.md 0.33kB
patient0185/Info_2CH.cfg 0.07kB
patient0185/Info_4CH.cfg 0.07kB
patient0184/patient0184_4CH_half_sequence_gt.nii.gz 98.75kB
patient0184/patient0184_4CH_half_sequence.nii.gz 4.44MB
patient0184/patient0184_4CH_ES_gt.nii.gz 3.91kB
patient0184/patient0184_4CH_ES.nii.gz 177.22kB
patient0184/patient0184_4CH_ED_gt.nii.gz 4.60kB
patient0184/patient0184_4CH_ED.nii.gz 191.10kB
patient0184/patient0184_2CH_half_sequence_gt.nii.gz 94.57kB
patient0184/patient0184_2CH_half_sequence.nii.gz 3.89MB
patient0184/patient0184_2CH_ES_gt.nii.gz 4.45kB
patient0184/patient0184_2CH_ES.nii.gz 170.06kB
patient0184/patient0184_2CH_ED_gt.nii.gz 4.53kB
patient0184/patient0184_2CH_ED.nii.gz 178.51kB
patient0184/MANDATORY_CITATION.md 0.33kB
patient0184/Info_2CH.cfg 0.07kB
patient0184/Info_4CH.cfg 0.07kB
patient0183/patient0183_4CH_half_sequence_gt.nii.gz 56.21kB
patient0183/patient0183_4CH_half_sequence.nii.gz 2.05MB
patient0183/patient0183_4CH_ES_gt.nii.gz 2.86kB
patient0183/patient0183_4CH_ES.nii.gz 101.81kB
patient0183/patient0183_4CH_ED_gt.nii.gz 3.13kB
patient0183/patient0183_4CH_ED.nii.gz 101.65kB
patient0183/patient0183_2CH_half_sequence_gt.nii.gz 63.53kB
patient0183/patient0183_2CH_half_sequence.nii.gz 2.53MB
patient0183/patient0183_2CH_ES_gt.nii.gz 2.75kB
patient0183/patient0183_2CH_ES.nii.gz 110.34kB
patient0183/patient0183_2CH_ED_gt.nii.gz 3.12kB
patient0183/patient0183_2CH_ED.nii.gz 109.31kB
patient0183/MANDATORY_CITATION.md 0.33kB
patient0183/Info_2CH.cfg 0.07kB
patient0183/Info_4CH.cfg 0.07kB
patient0182/patient0182_4CH_half_sequence_gt.nii.gz 82.08kB
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Type: Dataset
Tags:

Bibtex:
@article{,
title= {CAMUS Cardiac Acquisitions for Multi-structure Ultrasound Segmentation},
keywords= {},
author= {},
abstract= {The goal of this project is to provide all the materials to the community to resolve the problem of echocardiographic image segmentation and volume estimation from 2D ultrasound sequences (both two and four-chamber views). To this aim, the following solutions were set-up
introduction of the largest publicly-available and fully-annotated dataset for 2D echocardiographic assessment (to our knowledge). The CAMUS dataset, containing 2D apical four-chamber and two-chamber view sequences acquired from 500 patients, is made available for download

# Dataset properties

The overall CAMUS dataset consists of clinical exams from 500 patients, acquired at the University Hospital of St Etienne (France) and included in this study within the regulation set by the local ethical committee of the hospital after full anonymization. The acquisitions were optimized to perform left ventricle ejection fraction measurements. In order to enforce clinical realism, neither prerequisite nor data selection have been performed. Consequently,

- some cases were difficult to trace;

- the dataset involves a wide variability of acquisition settings;

- for some patients, parts of the wall were not visible in the images;

for some cases, the probe orientation recommendation to acquire a rigorous four-chambers view was simply impossible to follow and a five-chambers view was acquired instead. This produced a highly heterogeneous dataset, both in terms of image quality and pathological cases, which is typical of daily clinical practice data.

The dataset has been made available to the community HERE. The dataset comprises : i) a training set of 450 patients along with the corresponding manual references based on the analysis of one clinical expert; ii) a testing set composed of 50 new patients. The raw input images are provided through the raw/mhd file format.

# Study population

Half of the dataset population has a left ventricle ejection fraction lower than 45%, thus being considered at pathological risk (beyond the uncertainty of the measurement). Also, 19% of the images have a poor quality (based on the opinion of one expert), indicating that for this subgroup the localization of the left ventricle endocarium and left ventricle epicardium as well as the estimation of clinical indices are not considered clinically accurate and workable. In classical analysis, poor quality images are usually removed from the dataset because of their clinical uselessness. Therefore, those data were not involved in this project during the computation of the different metrics but were used to study their influence as part of the training and validation sets for deep learning techniques.

# Involved systems

The full dataset was acquired from GE Vivid E95 ultrasound scanners (GE Vingmed Ultrasound, Horten Norway), with a GE M5S probe (GE Healthcare, US). No additional protocol than the one used in clinical routine was put in place. For each patient, 2D apical four-chamber and two-chamber view sequences were exported from EchoPAC analysis software (GE Vingmed Ultrasound, Horten, Norway). These standard cardiac views were chosen for this study to enable the estimation of left ventricle ejection fraction values based on the Simpson’s biplane method of discs. Each exported sequence corresponds to a set of B-mode images expressed in polar coordinates. The same interpolation procedure was used to express all sequences in Cartesian coordinates with a unique grid resolution, i.e. λ/2 = 0.3 mm along the x-axis (axis parallel to the probe) and λ/4 = 0.15 mm along the z-axis (axis perpendicular to the probe), where λ corresponds to the wavelength of the ultrasound probe. At least one full cardiac cycle was acquired for each patient in each view, allowing manual annotation of cardiac structures at ED and ES.

This work has published to IEEE TMI journal. You must cite this paper for any use of the CAMUS database

```
S. Leclerc, E. Smistad, J. Pedrosa, A. Ostvik, et al.
"Deep Learning for Segmentation using an Open Large-Scale Dataset in 2D Echocardiography" in IEEE Transactions on Medical Imaging, vol. 38, no. 9, pp. 2198-2210, Sept. 2019.
```




https://i.imgur.com/aVBYSWH.jpg},
terms= {},
license= {},
superseded= {},
url= {https://www.creatis.insa-lyon.fr/Challenge/camus/}
}


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