Automated segmentation and quantification of the healthy and diseased aorta in CT angiographies using a dedicated deep learning approach.
Artificial intelligence
Computer-assisted, Aorta
Image processing
Neuronal networks
Journal
European radiology
ISSN: 1432-1084
Titre abrégé: Eur Radiol
Pays: Germany
ID NLM: 9114774
Informations de publication
Date de publication:
Jan 2022
Jan 2022
Historique:
received:
14
02
2021
accepted:
26
03
2021
pubmed:
26
6
2021
medline:
15
12
2021
entrez:
25
6
2021
Statut:
ppublish
Résumé
To develop and validate a deep learning-based algorithm for segmenting and quantifying the physiological and diseased aorta in computed tomography angiographies. CTA exams of the aorta of 191 patients (68.1 ± 14 years, 128 male), performed between 2015 and 2018, were retrospectively identified from our imaging archive and manually segmented by two investigators. A 3D U-Net model was trained on the data, which was divided into a training, a validation, and a test group at a ratio of 7:1:2. Cases in the test group (n = 41) were evaluated to compare manual and automatic segmentations. Dice similarity coefficient (DSC), mean surface distance (MSD), and Hausdorff surface distance (HSD) were extracted. Maximum diameter, effective diameter, and area were quantified and compared between both segmentations at eight anatomical landmarks, and at the maximum area of an aneurysms if present (n = 14). Statistics included error calculation, intraclass correlation coefficient, and Bland-Altman analysis. A DSC of 0.95 [0.94; 0.95] and an MSD of 0.76 [0.06; 0.99] indicated close agreement between segmentations. HSD was 8.00 [4.47; 10.00]. The largest absolute errors were found in the ascending aorta with 0.8 ± 1.5 mm for maximum diameter and at the coeliac trunk with - 30.0 ± 81.6 mm Automated segmentation of the aorta on CTA data using a deep learning algorithm is feasible and allows for accurate quantification of the aortic lumen even if the vascular architecture is altered by disease. • A deep learning-based algorithm can automatically segment the aorta, mostly within acceptable margins of error, even if the vascular architecture is altered by disease. • Quantifications performed in the segmentations were mostly within clinically acceptable limits, even in pathologically altered segments of the aorta.
Identifiants
pubmed: 34170365
doi: 10.1007/s00330-021-08130-2
pii: 10.1007/s00330-021-08130-2
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
690-701Informations de copyright
© 2021. European Society of Radiology.
Références
Roth GA, Huffman MD, Moran AE et al (2015) Global and regional patterns in cardiovascular mortality from 1990 to 2013. Circulation 132:1667–1678
doi: 10.1161/CIRCULATIONAHA.114.008720
Sakalihasan N, Limet R, Defawe OD (2005) Abdominal aortic aneurysm. Lancet 365:1577–1589
doi: 10.1016/S0140-6736(05)66459-8
Iezzi R, Goldberg SN, Merlino B, Posa A, Valentini V, Manfredi R (2019) Artificial intelligence in interventional radiology: a literature review and future perspectives. J Oncol 2019:6153041
doi: 10.1155/2019/6153041
Moccia S, De Momi E, El Hadji S, Mattos LS (2018) Blood vessel segmentation algorithms - review of methods, datasets and evaluation metrics. Comput Methods Programs Biomed 158:71–91
doi: 10.1016/j.cmpb.2018.02.001
Kamman AV, van Herwaarden JA, Orrico M et al (2016) Standardized protocol to analyze computed tomography imaging of type B aortic dissections. J Endovasc Ther 23:472–482
doi: 10.1177/1526602816642591
Hosny A, Parmar C, Quackenbush J, Schwartz LH, Aerts HJWL (2018) Artificial intelligence in radiology. Nat Rev Cancer 18:500–510
doi: 10.1038/s41568-018-0016-5
Cao L, Shi R, Ge Y et al (2019) Fully automatic segmentation of type B aortic dissection from CTA images enabled by deep learning. Eur J Radiol 121:108713
doi: 10.1016/j.ejrad.2019.108713
Lopez-Linares K, Aranjuelo N, Kabongo L et al (2018) Fully automatic detection and segmentation of abdominal aortic thrombus in post-operative CTA images using deep convolutional neural networks. Med Image Anal 46:202–214
doi: 10.1016/j.media.2018.03.010
Sedghi Gamechi Z, Bons LR, Giordano M et al (2019) Automated 3D segmentation and diameter measurement of the thoracic aorta on non-contrast enhanced CT. Eur Radiol 29:4613–4623
doi: 10.1007/s00330-018-5931-z
Zlahoda-Huzior A, Stanuch M, Witowski J, Dudek D (2019) Automatic aorta and left ventricle segmentation for TAVI procedure planning using convolutional neural networks. Conference Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2019:2777–2780
Hahn LD, Mistelbauer G, Higashigaito K et al (2020) CT-based true- and false-lumen segmentation in type B aortic dissection using machine learning. Radiol Cardiothorac Imaging 2:e190179
Yu Y, Gao Y, Wei J et al (2021) A three-dimensional deep convolutional neural network for automatic segmentation and diameter measurement of type B aortic dissection. Korean J Radiol 22:168–178
doi: 10.3348/kjr.2020.0313
Klein J, Wenzel M, Romberg D et al (2020) QuantMed: component-based deep learning platform for translational research. SPIE
Herman GT, Zheng J, Bucholtz CA (1992) Shape-based interpolation. IEEE Comput Graph Appl 12:69–79
doi: 10.1109/38.135915
Çiçek Ö, Abdulkadir A, Lienkamp SS, Brox T, Ronneberger O (2016) 3D U-Net: learning dense volumetric segmentation from sparse annotation. arXiv e-prints
Dice LR (1945) Measures of the amount of ecologic association between species. Ecology 26:297–302
doi: 10.2307/1932409
Gerig G, Jomier M, Chakos M (2001) Valmet: a new validation tool for assessing and improving 3D object segmentationproceedings of the 4th International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer-Verlag, pp 516–523
Cardenes R, de Luis-Garcia R, Bach-Cuadra M (2009) A multidimensional segmentation evaluation for medical image data. Comput Methods Programs Biomed 96:108–124
doi: 10.1016/j.cmpb.2009.04.009
Selle D, Preim B, Schenk A, Peitgen H (2002) Analysis of vasculature for liver surgical planning. IEEE Trans Med Imaging 21:1344–1357
doi: 10.1109/TMI.2002.801166
Koo TK, Li MY (2016) A guideline of selecting and reporting intraclass correlation coefficients for reliability research. J Chiropr Med 15:155–163
doi: 10.1016/j.jcm.2016.02.012
Krissian K, Malandain G, Ayache N (1997) Directional anisotropic diffusion applied to segmentation of vessels in 3D images. Springer, Berlin Heidelberg, pp 345–348
Frangi AF, Niessen WJ, Vincken KL, Viergever MA (1998) Multiscale vessel enhancement filtering. Springer, Berlin Heidelberg, pp 130–137
Wörz S, Tengg-Kobligk H, Henninger V et al (2010) 3-D quantification of the aortic arch morphology in 3-D CTA data for endovascular aortic repair. IEEE Trans Biomed Eng 57:2359–2368
doi: 10.1109/TBME.2010.2053539
Biesdorf A, Wörz S, Tengg-Kobligk Hv, Rohr K, Schnörr C (2015) 3D segmentation of vessels by incremental implicit polynomial fitting and convex optimization2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI), pp 1540–1543
Lee SH, Lee S (2015) Adaptive Kalman snake for semi-autonomous 3D vessel tracking. Comput Methods Programs Biomed 122:56–75
doi: 10.1016/j.cmpb.2015.06.008
Biesdorf A, Rohr K, Feng D et al (2012) Segmentation and quantification of the aortic arch using joint 3D model-based segmentation and elastic image registration. Med Image Anal 16:1187–1201
doi: 10.1016/j.media.2012.05.010
Ecabert O, Peters J, Walker MJ et al (2011) Segmentation of the heart and great vessels in CT images using a model-based adaptation framework. Med Image Anal 15:863–876
doi: 10.1016/j.media.2011.06.004
Tian Y, Chen Q, Wang W et al (2014) A vessel active contour model for vascular segmentation. Biomed Res Int 2014:106490
pubmed: 25101262
pmcid: 4101240
Quint LE, Liu PS, Booher AM, Watcharotone K, Myles JD (2013) Proximal thoracic aortic diameter measurements at CT: repeatability and reproducibility according to measurement method. Int J Cardiovasc Imaging 29:479–488
doi: 10.1007/s10554-012-0102-9
Lalys F, Esneault S, Castro M et al (2019) Automatic aortic root segmentation and anatomical landmarks detection for TAVI procedure planning. Minim Invasive Ther Allied Technol 28:157–164
doi: 10.1080/13645706.2018.1488734
Gao X, Boccalini S, Kitslaar PH et al (2019) A novel software tool for semi-automatic quantification of thoracic aorta dilatation on baseline and follow-up computed tomography angiography. Int J Cardiovasc Imaging 35:711–723
doi: 10.1007/s10554-018-1488-9
Brendan McMahan H, Moore E, Ramage D, Hampson S, Agüera y Arcas B (2016) Communication-efficient learning of deep networks from decentralized data. arXiv e-prints
Shokri R, Shmatikov V (2015) Privacy-Preserving Deep Learning Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security. Association for Computing Machinery, Denver, Colorado, USA, pp 1310–1321