Computerized Differentiation of Growth Status for Abdominal Aortic Aneurysms: A Feasibility Study

Rezaeitaleshmahalleh, Mostafa; Sunderland, Kevin; Lyu, Zonghan; Johnson, Tonie; King, Kristin; Liedl, David; Hofer, Janet M.; Wang, Min; Zhang, Xiaoming; Kuczmik, Wiktoria; Rasmussen, Todd E.; McBane, Robert D.; Jiang, Jingfeng

doi:10.1007/s12265-022-10352-8

Cited by 10 publications

(14 citation statements)

References 35 publications

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“…Smaller outlet vessels were removed to reduce the structural complexity, as we verified that the omission of those small secondary arteries has a negligible impact on aneurismal hemodynamics. This study followed the segmentation workflow done by Rezaeitaleshmahalleh et al [15], verified by our clinical collaborators (a cardiologist and a vascular surgeon) from the Mayo Clinic.…”

Section: Manual Annotationmentioning

confidence: 99%

“…The CFD workflow was verified with both phasecontrast magnetic resonance angiography (PC-MRA) [40,41] and ultrasound Doppler [42]. More detailed protocols are previously published and can be found elsewhere [15,19,43,44], and thus, we only provide essential information below.…”

Section: Volumetric Model and Cfd Simulationmentioning

confidence: 99%

“…In a few recent studies [12][13][14][15], when hemodynamic metric, morphological features, and patient health information were combined using machine learning, the prediction performance of AAAs' growth status was encouraging (i.e., the area under the curve [AUC] of receiving operating curve [ROC] > 0.8). In 'imagebased' computational hemodynamics [16][17][18][19], medical imaging data (e.g., computed tomography angiography [CTA], digital subtraction angiography [DSA], etc) from individual patients are used to create 'patient-specific' geometrical models.…”

Section: Introductionmentioning

confidence: 99%

“…A typical computational hemodynamics protocol uses manual segmentation to obtain vascular geometry. All four studies mentioned above used manual segmentation [12][13][14][15]. Nevertheless, manual segmentation is labor-intensive, timeconsuming, and affected by the operator's subjectivity.…”

Section: Introductionmentioning

confidence: 99%

“…(2) We investigate how varying structures of deep-learning image segmentation methods (i.e., two modifications of the classic U-Net model [27]) influence vessel geometry delineation and subsequent CFD results derived from those geometries. Since those simulated hemodynamic metrics are often used for predicting AAAs' growth status and intracranial aneurysm (IA) rupture status [12,15,28], changes in those hemodynamic metrics may have consequences.…”

Section: Introductionmentioning

confidence: 99%

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Deep-learning-based image segmentation for image-based computational hemodynamic analysis of abdominal aortic aneurysms: a comparison study

Lyu,

King,

Rezaeitaleshmahalleh

et al. 2023

Biomed. Phys. Eng. Express

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Computational hemodynamics is increasingly used to quantify hemodynamic characteristics in and around abdominal aortic aneurysms (AAA) in a patient-specific fashion. However, the time-consuming manual annotation hinders the clinical translation of computational hemodynamic analysis. Thus, we investigate the feasibility of using deep-learning-based image segmentation methods to reduce the time required for manual segmentation.Two of the latest deep-learning-based image segmentation methods, ARU-Net and CACU-Net, were used to test the feasibility of automated computer model creation for computational hemodynamic analysis. Morphological features and hemodynamic metrics of 30 computed tomography angiography (CTA) scans were compared between pre-dictions and manual models.The DICE score for both networks was 0.916, and the correlation value was above 0.95, indicating their ability to generate models comparable to human segmentation. The Bland-Altman analysis shows a good agreement between deep learning and manual segmentation results. Compared with manual (computational hemodynamics) model recreation, the time for automated computer model generation was significantly reduced (from ~2hr to ~10min).Automated image segmentation can significantly reduce time expenses on the recreation of patient-specific AAA models. Moreover, our study showed that both CACU-Net and ARU-Net could accomplish AAA segmentation, and CACU-Net outperformed ARU-Net in terms of accuracy and time-saving.

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Section: Manual Annotationmentioning

confidence: 99%