Using deep learning to segment cardiovascular four-dimensional MRI This project aims to develop and evaluate a deep-learning technique to segment MR images that include the four cardiac chambers and great vessels in four-dimensional image data (1). The technique will be developed and evaluated on a set of ~150 already acquired 4D flow MRI datasets, that have been segmented using a validated multi-atlas based registration technique. This multi-atlas based technique resulted in high quality segmentations, with the disadvantage of long computation times typically required by three-dimensional registration techniques. In contrast, a deep learning based method would require a large amount of resources and time during training, but would generate results much faster once the tool is assimilated into the clinical process. Additionally, a convolutional neural network trained using a sufficiently diverse set of images should be able to handle more extreme morphological differences between subjects when compared to atlas-based segmentation methods. A previously evaluated method based on atlas segmentation has been used to generate labels for the aorta, pulmonary artery, and the four cardiac chambers at each timeframe for all dataset (2). Evaluation of the results will be done using commonly employed metrics for segmentation evaluation, such as Dice coefficient and Hausdorff distance; and also metrics based on quantitative physiological measures, such as stroke volume of the cardiac chambers and flow volume through the great vessels. 4D phase-contrast MRI datasets corresponding to ~150 subjects have already been acquired and are available and ethically approved to use for this project. Each dataset is composed of a volume of size around 112x112x50x40, which can be sliced into 3D volumes of different shapes for training and validation purposes. Figure 1: Atlas-based segmentation generated for one dataset at end-diastole (a, c) and end-systole (b, d). Visualized as an isosurface rendering (a, b), and superimposed over a 4-chamber MRI of the heart (c, d). References 1. Markl M, Frydrychowicz A, Kozerke S, Hope M, Wieben O. 4D flow MRI. J Magn Reson Imaging [Internet]. 2012;36. Available from: http://dx.doi.org/10.1002/jmri.23632 2. Bustamante M, Gupta V, Forsberg D, Carlhäll CJ, Engvall J, Ebbers T. Automated multi-atlas segmentation of cardiac 4D flow MRI. Med Image Anal. 2018; Learn more about our research at: https://liu.se/en/research/cardiovascular-magnetic-resonance-group LV RV LA RA LV RV LA RA Ao Ao a. b. c. d.

Using deep learning to segment cardiovascular four-dimensional … · This project aims to develop and evaluate a deep-learning technique to segment MR images that include the four

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Page 1: Using deep learning to segment cardiovascular four-dimensional … · This project aims to develop and evaluate a deep-learning technique to segment MR images that include the four

Using deep learning to segment cardiovascular four-dimensional MRI

This project aims to develop and evaluate a deep-learning technique to segment MR images that include the four cardiac chambers and great vessels in four-dimensional image data (1). The technique will be developed and evaluated on a set of ~150 already acquired 4D flow MRI datasets, that have been segmented using a validated multi-atlas based registration technique. This multi-atlas based technique resulted in high quality segmentations, with the disadvantage of long computation times typically required by three-dimensional registration techniques. In contrast, a deep learning based method would require a large amount of resources and time during training, but would generate results much faster once the tool is assimilated into the clinical process. Additionally, a convolutional neural network trained using a sufficiently diverse set of images should be able to handle more extreme morphological differences between subjects when compared to atlas-based segmentation methods.

A previously evaluated method based on atlas segmentation has been used to generate labels for the aorta, pulmonary artery, and the four cardiac chambers at each timeframe for all dataset (2). Evaluation of the results will be done using commonly employed metrics for segmentation evaluation, such as Dice coefficient and Hausdorff distance; and also metrics based on quantitative physiological measures, such as stroke volume of the cardiac chambers and flow volume through the great vessels.

4D phase-contrast MRI datasets corresponding to ~150 subjects have already been acquired and are available and ethically approved to use for this project. Each dataset is composed of a volume of size around 112x112x50x40, which can be sliced into 3D volumes of different shapes for training and validation purposes.

Figure 1: Atlas-based segmentation generated for one dataset at end-diastole (a, c) and end-systole (b, d). Visualized as an isosurface rendering (a, b), and superimposed over a 4-chamber MRI of the heart (c, d).

References

1. Markl M, Frydrychowicz A, Kozerke S, Hope M, Wieben O. 4D flow MRI. J Magn Reson Imaging [Internet]. 2012;36. Available from: http://dx.doi.org/10.1002/jmri.23632

2. Bustamante M, Gupta V, Forsberg D, Carlhäll CJ, Engvall J, Ebbers T. Automated multi-atlas segmentation of cardiac 4D flow MRI. Med Image Anal. 2018;

Learn more about our research at: https://liu.se/en/research/cardiovascular-magnetic-resonance-group

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