Deep Learning to Automatically Segment and Analyze Abdominal Aortic Aneurysm from Computed Tomography Angiography

Brutti, Francesca; Fantazzini, Alice; Finotello, Alice; Müller, Lucas O.; Auricchio, Ferdinando; Pane, Bianca; Spinella, Giovanni; Conti, Michele

doi:10.1007/s13239-021-00594-z

Cited by 19 publications

(10 citation statements)

References 19 publications

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“…The following considerations were taken into account in designing the model to improve the existing limitations of recent studies. Most of the recent articles started by training a deep learning model to segment and extract the ILT/wall as a first step (24)(25)(26). The ILT is a complex tissue with highly inconsistent properties.…”

Section: Discussionmentioning

confidence: 99%

“…One of the clearest advantages is that extensive pre-processing is not necessary when using FCNs. Another advantage of our method with respect to existing FCN studies (24)(25)(26) is the automatic extraction of the ROI using semantic segmentation, which greatly simplified the subsequent steps of segmenting the structures inside the aorta.…”

Section: Discussionmentioning

confidence: 99%

“…In another recent study, thrombus segmentation using a region-based convolutional neural network was performed by Hwang et al ( 25 ). A thrombus segmentation model followed by lumen segmentation using a U-net was proposed by Brutti et al ( 26 ). Most of these recent studies approach the segmentation by directly segmenting the thrombus first.…”

Section: Introductionmentioning

confidence: 99%

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Deep-learning method for fully automatic segmentation of the abdominal aortic aneurysm from computed tomography imaging

Abdolmanafi¹,

Forneris

Moore

et al. 2023

Front. Cardiovasc. Med.

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Abdominal aortic aneurysm (AAA) is one of the leading causes of death worldwide. AAAs often remain asymptomatic until they are either close to rupturing or they cause pressure to the spine and/or other organs. Fast progression has been linked to future clinical outcomes. Therefore, a reliable and efficient system to quantify geometric properties and growth will enable better clinical prognoses for aneurysms. Different imaging systems can be used to locate and characterize an aneurysm; computed tomography (CT) is the modality of choice in many clinical centers to monitor later stages of the disease and plan surgical treatment. The lack of accurate and automated techniques to segment the outer wall and lumen of the aneurysm results in either simplified measurements that focus on few salient features or time-consuming segmentation affected by high inter- and intra-operator variability. To overcome these limitations, we propose a model for segmenting AAA tissues automatically by using a trained deep learning-based approach. The model is composed of three different steps starting with the extraction of the aorta and iliac arteries followed by the detection of the lumen and other AAA tissues. The results of the automated segmentation demonstrate very good agreement when compared to manual segmentation performed by an expert.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Deep-learning method for fully automatic segmentation of the abdominal aortic aneurysm from computed tomography imaging

Abdolmanafi¹,

Forneris

Moore

et al. 2023

Front. Cardiovasc. Med.

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“…In addition to the problem of manual measurements of outer-to-outer wall diameter, another challenge for most sizing software-including Dongmai-is detecting and segmenting a thrombus. Intraluminal thrombi exist in over 75% of AAA cases [13]; however, most sizing software can only detect the aortic lumen areas enhanced using a contrast medium, and the thrombus needs to be segmented manually by the user; this process can be time-consuming and challenging [14]. Some studies have proposed semiautomated methods [15], whereas others have employed deeplearning approaches for automatic thrombus segmentation [13].…”

Section: Discussionmentioning

confidence: 99%

A new semiautomated three-dimensional sizing software product can reliably measure the length and diameter of abdominal aortic aneurysms

Xiao

Zhang

Yang

et al. 2023

Preprint

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Purpose Inaccurate preoperative assessment of aortic–iliac dimensions before endovascular aortic aneurysm repair (EVAR) may result in incorrect stent graft sizing and other problems. We aimed to determine whether the sizing method performed using Dongmai is as precise and reproducible as that performed by an experienced radiologist who provides manual measurements of the length and diameter of abdominal aortic aneurysms (AAAs). Methods In this retrospective study, the EVAR preoperative parameters were compared using intra-observer, interobserver, and Dongmai/radiological workstation comparison tests. Intraclass correlation coefficients (ICCs), mean differences, agreement limits, and absolute differences (≤ 2 mm and ≤ 5mm) were used to analyze the data. Results We enrolled 31 patients (24 men, 7 women; mean age: 69 ± 2.9 years) with AAA who were treated with EVAR. To determine intra-observer reliability, various parameters were repeatedly measured using Dongmai by a single user; all ICCs were > 0.9, indicating excellent reliability. The percentages of diameters and length that met the conditions were at least 91% and 77% with absolute differences of ≤ 2 mm, respectively, and 96% (both diameter and length) with absolute difference of ≤ 5 mm. Interobserver reliability assesses measurement agreement between two Dongmai users; the results were similar with those of intra-observer analysis. For the Dongmai/radiological workstation, ICC, mean differences, and agreement limits were similar between intra- and interobserver analyses. No statistically significant difference was observed among the results of the three analyses. Conclusion We determined the reliability of length and diameter measurements using Dongmai during preoperative assessments for EVAR.

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“…Computed tomography (CT) scans are used in clinical practice to assess, manage, and monitor AAA after an initial discovery during screening [3]. In recent years segmentation models based on deep neural networks have demonstrated great performance for automatized CT aorta segmentation, and numerous studies have been trained on large, expert annotated, and publicly available datasets [17,1,10,2]. This leads to the possible application of automatic screen-ing and monitoring of AAA in CT imaging using deep learning [21].…”

Section: ) Domain Adaptationmentioning

confidence: 99%

CACTUSS: Common Anatomical CT-US Space for US examinations

Velikova¹,

Simson²,

Salehi³

et al. 2022

Preprint

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Abdominal aortic aneurysm (AAA) is a vascular disease in which a section of the aorta enlarges, weakening its walls and potentially rupturing the vessel. Abdominal ultrasound has been utilized for diagnostics, but due to its limited image quality and operator dependency, CT scans are usually required for monitoring and treatment planning. Recently, abdominal CT datasets have been successfully utilized to train deep neural networks for automatic aorta segmentation. Knowledge gathered from this solved task could therefore be leveraged to improve US segmentation for AAA diagnosis and monitoring. To this end, we propose CACTUSS: a common anatomical CT-US space, which acts as a virtual bridge between CT and US modalities to enable automatic AAA screening sonography. CACTUSS makes use of publicly available labelled data to learn to segment based on an intermediary representation that inherits properties from both US and CT. We train a segmentation network in this new representation and employ an additional image-to-image translation network which enables our model to perform on real B-mode images. Quantitative comparisons against fully supervised methods demonstrate the capabilities of CACTUSS in terms of Dice Score and diagnostic metrics, showing that our method also meets the clinical requirements for AAA scanning and diagnosis.

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Deep Learning to Automatically Segment and Analyze Abdominal Aortic Aneurysm from Computed Tomography Angiography

Cited by 19 publications

References 19 publications

Deep-learning method for fully automatic segmentation of the abdominal aortic aneurysm from computed tomography imaging

Deep-learning method for fully automatic segmentation of the abdominal aortic aneurysm from computed tomography imaging

A new semiautomated three-dimensional sizing software product can reliably measure the length and diameter of abdominal aortic aneurysms

CACTUSS: Common Anatomical CT-US Space for US examinations

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