Francesca Brutti1, Alice Fantazzini2,3, Alice Finotello4, Lucas Omar Müller1, Ferdinando Auricchio5, Bianca Pane6, Giovanni Spinella6, Michele Conti7. 1. Department of Mathematics, University of Trento, Trento, Italy. 2. Department of Experimental Medicine, University of Genoa, Genoa, Italy. 3. Camelot Biomedical Systems S.r.l., Genoa, Italy. 4. Department of Integrated Surgical and Diagnostic Sciences, University of Genoa, Genoa, Italy. 5. Department of Civil Engineering and Architecture, University of Pavia, Via Ferrata 3, 27100, Pavia, Italy. 6. Vascular and Endovascular Surgery Unit, IRCCS Ospedale Policlinico San Martino, University of Genoa, Genoa, Italy. 7. Department of Civil Engineering and Architecture, University of Pavia, Via Ferrata 3, 27100, Pavia, Italy. michele.conti@unipv.it.
Abstract
PURPOSE: Although segmentation of Abdominal Aortic Aneurysms (AAA) thrombus is a crucial step for both the planning of endovascular treatment and the monitoring of the intervention's outcome, it is still performed manually implying time consuming operations as well as operator dependency. The present paper proposes a fully automatic pipeline to segment the intraluminal thrombus in AAA from contrast-enhanced Computed Tomography Angiography (CTA) images and to subsequently analyze AAA geometry. METHODS: A deep-learning-based pipeline is developed to localize and segment the thrombus from the CTA scans. The thrombus is first identified in the whole sub-sampled CTA, then multi-view U-Nets are combined together to segment the thrombus from the identified region of interest. Polygonal models are generated for the thrombus and the lumen. The lumen centerline is automatically extracted from the lumen mesh and used to compute the aneurysm and lumen diameters. RESULTS: The proposed multi-view integration approach returns an improvement in thrombus segmentation with respect to the single-view prediction. The thrombus segmentation model is trained over a training set of 63 CTA and a validation set of 8 CTA scans. By comparing the thrombus segmentation predicted by the model with the ground truth data, a Dice Similarity Coefficient (DSC) of 0.89 ± 0.04 is achieved. The AAA geometry analysis provided an Intraclass Correlation Coefficient (ICC) of 0.92 and a mean-absolute difference of 3.2 ± 2.4 mm, for the measurements of the total diameter of the aneurysm. Validation of both thrombus segmentation and aneurysm geometry analysis is performed over a test set of 14 CTA scans. CONCLUSION: The developed deep learning models can effectively segment the thrombus from patients affected by AAA. Moreover, the diameters automatically extracted from the AAA show high correlation with those manually measured by experts.
PURPOSE: Although segmentation of Abdominal Aortic Aneurysms (AAA) thrombus is a crucial step for both the planning of endovascular treatment and the monitoring of the intervention's outcome, it is still performed manually implying time consuming operations as well as operator dependency. The present paper proposes a fully automatic pipeline to segment the intraluminal thrombus in AAA from contrast-enhanced Computed Tomography Angiography (CTA) images and to subsequently analyze AAA geometry. METHODS: A deep-learning-based pipeline is developed to localize and segment the thrombus from the CTA scans. The thrombus is first identified in the whole sub-sampled CTA, then multi-view U-Nets are combined together to segment the thrombus from the identified region of interest. Polygonal models are generated for the thrombus and the lumen. The lumen centerline is automatically extracted from the lumen mesh and used to compute the aneurysm and lumen diameters. RESULTS: The proposed multi-view integration approach returns an improvement in thrombus segmentation with respect to the single-view prediction. The thrombus segmentation model is trained over a training set of 63 CTA and a validation set of 8 CTA scans. By comparing the thrombus segmentation predicted by the model with the ground truth data, a Dice Similarity Coefficient (DSC) of 0.89 ± 0.04 is achieved. The AAA geometry analysis provided an Intraclass Correlation Coefficient (ICC) of 0.92 and a mean-absolute difference of 3.2 ± 2.4 mm, for the measurements of the total diameter of the aneurysm. Validation of both thrombus segmentation and aneurysm geometry analysis is performed over a test set of 14 CTA scans. CONCLUSION: The developed deep learning models can effectively segment the thrombus from patients affected by AAA. Moreover, the diameters automatically extracted from the AAA show high correlation with those manually measured by experts.
Authors: Luca Antiga; Marina Piccinelli; Lorenzo Botti; Bogdan Ene-Iordache; Andrea Remuzzi; David A Steinman Journal: Med Biol Eng Comput Date: 2008-11-11 Impact factor: 2.602
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Authors: Elliot L Chaikof; Ronald L Dalman; Mark K Eskandari; Benjamin M Jackson; W Anthony Lee; M Ashraf Mansour; Tara M Mastracci; Matthew Mell; M Hassan Murad; Louis L Nguyen; Gustavo S Oderich; Madhukar S Patel; Marc L Schermerhorn; Benjamin W Starnes Journal: J Vasc Surg Date: 2018-01 Impact factor: 4.268