Comparative Analysis of Artery Anatomy Evaluated by Postmortem Tomography, CT Angiography, and Postmortem and Predeath CT Scans

Wolosker, M; Leiderman, Dafne Braga Diamante; Estevan, Fernanda Alvarenga; Wolosker, Nelson; Zerati, Antônio Eduardo; Amaro, Edson

doi:10.1016/j.avsg.2020.09.005

Cited by 2 publications

(2 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Several studies have attempted to assess the incidence of TAAs 5 . However, the key point for disease determination is dependent upon the average aortic size in a given population, which is broadly variable 1,6,7 . TAA diameter determination is also paramount for the decision-making process regarding surgical management 8 , with current guidelines proposing surgical treatment for dilations over 4cm in the ascending aorta and over 6cm in the descending segment, though these may vary according to comorbidities 1 .…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence for automated thoracic aorta diameter measurement using different computed tomography protocols

Portugal

Pinheiro

Lee

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

This study aimed to develop an automated 3-dimensional (3D) segmentation method for measuring the diameter of the thoracic aorta using different computed tomography (CT) protocols. A total of 587 CT scans were retrospectively analysed, and a manual slice-by-slice segmentation of the thoracic aorta was performed by three specialists. The segmented images were used to train convolutional neural network (CNN) models for automated segmentation. The models achieved high accuracy, with an average Dice Score Coefficient (DSC) of 0.8708. Four different methods for thoracic aorta diameter measurement were compared: manual measuring, semi-automatic measuring, automatic measuring using PyRadiomics, and automatic measuring using a made-to-measure algorithm. The results showed that the automatic measuring methods had similar accuracy to the manual and semi-automatic methods. The mean thoracic aorta diameter varied between 3.3 cm and 4.95 cm. These findings demonstrate the feasibility and accuracy of using artificial intelligence algorithms for automated thoracic aorta diameter measurement, which can aid in the assessment and management of aortic diseases.

show abstract

Section: Introductionmentioning

confidence: 99%

Artificial intelligence for automated thoracic aorta diameter measurement using different computed tomography protocols

Portugal

Pinheiro

Lee

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

Section: Introductionmentioning

confidence: 99%

Artificial intelligence for automated thoracic aorta diameter measurement using different computed tomography protocols

Portugal

Pinheiro

Lee

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

Introduction: Thoracic aortic aneurysm diameter determination is paramount for the decision-making process regarding surgical management. Studies focusing in asymptomatic patients have determined prevalence of 0.16 to 0.36% of TAAs in imaging studies. Several groups have proposed automated aortic measurement tools as propaedeutic and therapeutic instruments. In this study we developed and tested an automatic 3-dimensional (3D) segmentation method for the thoracic aorta, applicable on computed tomography angiography (CTA) acquired using low-dose and standard dose protocol, with and without contrast enhancement; and to accurately calculate the 3D diameter information of the arterial segments. Methods: a retrospective cohort of all CT scans acquired in our service between 2016 and 2021 led to the selection of 587 CT exams including low and standard-dose radiation, with and without contrast enhancement. 527 exams were used for neural network training of an algorithm capable of aptly measuring the aortic diameters, using manual measurements performed by three medical specialists as a baseline. Sixty exams were used for validation. The algorithm was developed both for use with the support of PyRadiomics and for a self-made approach. Results: Aortic measurement using the algorithm supported by PyRadiomics resulted in mean absolute error values under 2mm. For the self-made approach, mean absolute error values were under 5mm. Conclusion: This study presents an effective automated solution for thoracic aortic measurement with good results in sets of standard or low-radiation exams, as well as those acquired with or without contrast enhancement; presenting a possibility for an auxiliary tool for automation of the process of measuring the diameter of the thoracic aorta.

show abstract

Comparative Analysis of Artery Anatomy Evaluated by Postmortem Tomography, CT Angiography, and Postmortem and Predeath CT Scans

Cited by 2 publications

References 17 publications

Artificial intelligence for automated thoracic aorta diameter measurement using different computed tomography protocols

Artificial intelligence for automated thoracic aorta diameter measurement using different computed tomography protocols

Artificial intelligence for automated thoracic aorta diameter measurement using different computed tomography protocols

Contact Info

Product

Resources

About