# Segmentation of 4D Flow MRI: Comparison between 3D Deep Learning and Velocity-Based Level Sets

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## Abstract

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## 1. Introduction

#### 1.1. Objective

#### 1.2. Related Work

## 2. Materials and Methods

#### 2.1. Study Population

#### 2.2. 4D Flow MRI Acquisition Settings

#### 2.3. Ground Truth

#### 2.4. Pre-Processing and Post-Processing

#### 2.5. Segmentation with Level Sets

#### 2.6. Segmentation Using Deep Learning

#### 2.7. Evaluation

## 3. Results

## 4. Discussion

## 5. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## References

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**Figure 2.**The 3D U-Net architecture used for the deep learning-based segmentation. The architecture was trained using the leave-one-patient-out strategy. The input image for each patient was a volume taken from a magnitude image of the systolic phase. The size of the input volume was set to 146 × 176 × 44. The numbers above the blocks indicate the number of feature maps.

**Figure 3.**The examples of the segmentations obtained using the two approaches. The first column presents a case where both methods provided satisfactory results. The second column presents a case where both methods provided poor results. From top to bottom: ground truth data; segmentation using the U-Net network; segmentation based on the level set approach.

**Table 1.**The comparison between the manual and automatic segmentations obtained using the U-Net- and level set-based approaches. The best results are highlighted in bold.

Metric | Region | U-Net | Level Set | p-Value |
---|---|---|---|---|

DSC | Whole Aorta | 0.92 ± 0.02 | 0.86 ± 0.05 | <${10}^{-5}$ |

Ascending Aorta | 0.93 ± 0.02 | 0.88 ± 0.02 | <${10}^{-5}$ | |

Descending Aorta | 0.93 ± 0.02 | 0.85 ± 0.08 | <${10}^{-5}$ | |

Abdominal Aorta | 0.84 ± 0.09 | 0.72 ± 0.22 | 0.005 | |

HD (mm) | Whole Aorta | 21.49 ± 24.8 | 35.79 ± 31.33 | 0.002 |

Ascending Aorta | 9.63 ± 3.4 | 12.39 ± 4.84 | 0.013 | |

Descending Aorta | 5.97 ± 6.39 | 7.11 ± 3.99 | 0.063 | |

Abdominal Aorta | 16.38 ± 13.6 | 30.98 ± 27.31 | 0.002 | |

Max WSS Absolute Difference (Pa) | Whole Aorta | 0.754 ± 1.07 | 0.737 ± 0.79 | 0.907 |

Ascending Aorta | 0.743 ± 1.08 | 0.742 ± 0.79 | 0.992 | |

Descending Aorta | 0.116 ± 0.14 | 0.133 ± 0.11 | 0.526 | |

Abdominal Aorta | 0.155 ± 0.26 | 0.112 ± 0.11 | 0.354 |

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**MDPI and ACS Style**

Barrera-Naranjo, A.; Marin-Castrillon, D.M.; Decourselle, T.; Lin, S.; Leclerc, S.; Morgant, M.-C.; Bernard, C.; De Oliveira, S.; Boucher, A.; Presles, B.;
et al. Segmentation of 4D Flow MRI: Comparison between 3D Deep Learning and Velocity-Based Level Sets. *J. Imaging* **2023**, *9*, 123.
https://doi.org/10.3390/jimaging9060123

**AMA Style**

Barrera-Naranjo A, Marin-Castrillon DM, Decourselle T, Lin S, Leclerc S, Morgant M-C, Bernard C, De Oliveira S, Boucher A, Presles B,
et al. Segmentation of 4D Flow MRI: Comparison between 3D Deep Learning and Velocity-Based Level Sets. *Journal of Imaging*. 2023; 9(6):123.
https://doi.org/10.3390/jimaging9060123

**Chicago/Turabian Style**

Barrera-Naranjo, Armando, Diana M. Marin-Castrillon, Thomas Decourselle, Siyu Lin, Sarah Leclerc, Marie-Catherine Morgant, Chloé Bernard, Shirley De Oliveira, Arnaud Boucher, Benoit Presles,
and et al. 2023. "Segmentation of 4D Flow MRI: Comparison between 3D Deep Learning and Velocity-Based Level Sets" *Journal of Imaging* 9, no. 6: 123.
https://doi.org/10.3390/jimaging9060123