# Segmentation and characterization of the aorta and supra-aortic trunks for neuroradiology procedure planning

Mounir Lahlouh, Yasmina Chenoune, Raphaël Blanc, Michel P. Piotin, Simon Escalard, Robert Fahed, Jérome Szewczyk, Nicolas Passat

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## ► To cite this version:

Mounir Lahlouh, Yasmina Chenoune, Raphaël Blanc, Michel P. Piotin, Simon Escalard, et al. Segmentation and characterization of the aorta and supra-aortic trunks for neuroradiology procedure planning. Colloque Français d'Intelligence Artificielle en Imagerie Biomédicale (IABM), Mar 2023, Paris, France. . hal-03932761

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Submitted on 4 Apr 2023

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3iA Côte d'Azur
Institut interdisciplinaire
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PR[AI]RIE
PaRis Artificial Intelligence Research Institute

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institut
Curie

IABM 2023 - Colloque Français d'Intelligence Artificielle en Imagerie
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30-31 March 2023, Institut Curie, Paris, France

# SEGMENTATION AND CHARACTERIZATION OF THE AORTA AND SUPRA-AORTIC TRUNKS FOR NEURORADIOLOGY PROCEDURE PLANNING

Mounir Lahlouh<fnref n="1" />,<fnref n="2" />,<fnref n="3" />, Yasmina Chenoune<fnref n="2" />,<fnref n="4" />, Raphaël Blanc<fnref n="3" />,<fnref n="5" />, Michel Piotin<fnref n="5" />, Simon Escalard<fnref n="5" />, Robert Fahed<fnref n="5" />,<fnref n="6" />, Jérôme Szewczyk<fnref n="3" />,<fnref n="7" />, Nicolas Passat<fnref n="1" />

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## 1. Clinical background

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<figure><img src="image_12.png" /><figcaption>Madhwal's aortic arch anatomy classification [1]</figcaption></figure>

The type of aortic arch provides information on the complexity of navigation to the pathological area and helps to better plan procedures and choose the appropriate catheter.

## 2. Technical background

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<table><thead><tr><th>Task</th><th>Studies</th></tr></thead><tbody><tr><td>SEGMENTATION</td><td><ul><li>Studies are oriented towards the use of CTA volumes.</li><li>U-Net variants are the most used architectures.</li><li>Approaches like multi-view 2D CNNs [2] and multi-stage pipelines were used [3].</li><li>Segmentation is often pathology oriented e.g. segmentation of type B aortic dissection [3].</li></ul></td></tr><tr><td>CHARACTERIZATION</td><td><ul><li>Generic frameworks with sets of functions for geometric quantification of anatomical features and vessel characterization [4, 5]. These frameworks are suitable for engineers for research but not relevant for clinicians to use in a medical setting.</li></ul></td></tr></tbody></table>

## 3. Data

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22 patients underwent 3T MR exams of aortic arch, supra-aortic branches and intracranial vessels on Philips imaging system (Ingenia 3T, Best, The Netherlands).

Acquisition parameters: coronal plane covering: 250 slices; active TR/TE = 5.5/2.2 ms; flip angle = 27°; number of excitations = 1; acquisition bandwidth = 476.7 Hz; FOV = 380 x 321; voxel size = 0.5 x 0.5 x 1.0 mm; imaging time = 69 sec; Bolus track injection technique.

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## References

[1] S. Madhwal, V. Rajagopal, D. L. Bhatt, C. T. Bajzer, P. Whitlow, and S. R. Kapadia, "Predictors of difficult carotid stenting as determined by aortic arch angiography," *J Invasive Cardiol*, vol. 20, pp. 200-204, 2008.

[2] A. Fantazzini et al., "3D Automatic Segmentation of Aortic Computed Tomography Angiography Combining Multi-View 2D Convolutional Neural Networks," *Cardiovasc Eng Technol*, vol. 11, no. 5, pp. 576-586, 2020.

[3] D. Chen et al., "Multi-stage learning for segmentation of aortic dissections using a prior aortic anatomy simplification," *Med Image Anal*, vol. 69, p. 101931, 2021.

[4] M. Piccinelli, A. Veneziani, D. A. Steinman, A. Remuzzi, and L. Antiga, "A framework for geometric analysis of vascular structures: Application to cerebral aneurysms," *IEEE Trans Med Imaging*, vol.28, pp. 1141-1155, 2009.

[5] H. A. Kjeldsberg, A. W. Bergersen, and K. Valen-Sendstad, "morphMan: Automated manipulation of vascular geometries," *J Open Source Soft*, vol. 4, 2019.

## 4. Segmentation and characterization pipeline

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## 5. Results

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<table><caption>Ground truth</caption><thead><tr><th>Arch type</th><th>Precision</th><th>Recall</th><th>F1-score</th></tr></thead><tbody><tr><td>Type I</td><td>1.00</td><td>1.00</td><td>1.00</td></tr><tr><td>Type II</td><td>0.80</td><td>1.00</td><td>0.89</td></tr><tr><td>Type III</td><td>1.00</td><td>0.92</td><td>0.96</td></tr></tbody></table>

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<table><thead><tr><th rowspan="2">Artery</th><th colspan="3">Length</th><th colspan="3">Tortuosity</th></tr><tr><th>Pearson</th><th>RMSE</th><th>MAPE</th><th>Pearson</th><th>RMSE</th><th>MAPE</th></tr></thead><tbody><tr><td>RCCA</td><td>0.97</td><td>5.97</td><td>1.75</td><td>0.93</td><td>0.06</td><td>3.32</td></tr><tr><td>LCCA</td><td>0.92</td><td>6.80</td><td>1.74</td><td>0.95</td><td>0.04</td><td>2.30</td></tr></tbody></table>

Reverse curves:

* RCCA: Pearson correlation = 0.88

* LCCA: Pearson correlation = 0.96

## 6. Conclusion and perspectives

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* Complete analysis pipeline, from MRA images to high-level features: arch type, tortuosities, reverse curves, take-off angles and access difficulty after engaging an artery.

* Quantitative and qualitative validation by comparison to the medical reference (manual measurements and segmentations).

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HÔPITAL FONDATION
Adolphe de ROTHSCHILD
LA RÉFÉRENCE TÊTE ET COU

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basecamp
vascular

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UNIVERSITÉ DE REIMS
CHAMPAGNE-ARDENNE

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CReSTIC

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<footnote n="1">¹ CReSTIC, Université de Reims Champagne-Ardenne, Reims, France</footnote>

<footnote n="2">² ESME Research Lab, Paris, France</footnote>

<footnote n="3">³ Basecamp Vascular, Paris, France</footnote>

<footnote n="4">⁴ LISSI, Université Paris-Est, Vitry-sur-Seine, France</footnote>

<footnote n="5">⁵ Fondation Ophtalmologique de Rothschild, Interventional Neuroradiology Department, Paris, France</footnote>

<footnote n="6">⁶ Department of Medicine, Division of Neurology, The Ottawa Hospital, Ottawa Hospital Research Institute and University of Ottawa, Ottawa, Ontario, Canada</footnote>

<footnote n="7">⁷ ISIR, Sorbonne Université, Paris, France</footnote>