Automatic Detection and Segmentation of Thrombi in Abdominal Aortic Aneurysms Using a Mask Region-Based Convolutional Neural Network with Optimized Loss Functions

Hwang, Byunghoon; Kim, Jihu; Lee, Sungmin; Kim, Eun Young; Kim, Jeong Ho; Jung, Younhyun; Hwang, Hyoseok

doi:10.3390/s22103643

Cited by 17 publications

(9 citation statements)

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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%

“…Finally, there are a few published examples of deep convolutional neural networks to segment the aorta, for example, the work by López-Linares et al ( 24 ). 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 ).…”

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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“…López-Linares et al [ 13 ] firstly introduced the modified holistically-nested edge detection (HED) CNN network to improve the boundary delineation of thrombus ROIs. The use of well-established Mask R-CNN was also investigated by Hwang et al [ 18 ], where an optimized cost function was developed to reinforce the segmentation results. All these 2D-based methods carried out the segmentation independently for each image and lacked the features commonly available from adjacent images.…”

Section: Related Workmentioning

confidence: 99%

“…This coherence learning capability can be useful for challenging situations, for example, where a target image is severe with inherent postoperative artifacts and noises and may need the adjacent images to learn more robust features for thrombus ROIs. For the spatial feature extraction, we use mask region-based CNN (Mask R-CNN) as suggested by Hwang et al [ 18 ]. We experiment the segmentation capability and algorithmic property of our Bi-CLSTM-based method using 60 patient studies of AAA (i.e., postoperative CTA image volumes), which is the largest postoperative AAA dataset to our knowledge.…”

Section: Introductionmentioning

confidence: 99%

Abdominal Aortic Thrombus Segmentation in Postoperative Computed Tomography Angiography Images Using Bi-Directional Convolutional Long Short-Term Memory Architecture

Jung

Kim

et al. 2022

Sensors

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Abdominal aortic aneurysm (AAA) is a fatal clinical condition with high mortality. Computed tomography angiography (CTA) imaging is the preferred minimally invasive modality for the long-term postoperative observation of AAA. Accurate segmentation of the thrombus region of interest (ROI) in a postoperative CTA image volume is essential for quantitative assessment and rapid clinical decision making by clinicians. Few investigators have proposed the adoption of convolutional neural networks (CNN). Although these methods demonstrated the potential of CNN architectures by automating the thrombus ROI segmentation, the segmentation performance can be further improved. The existing methods performed the segmentation process independently per 2D image and were incapable of using adjacent images, which could be useful for the robust segmentation of thrombus ROIs. In this work, we propose a thrombus ROI segmentation method to utilize not only the spatial features of a target image, but also the volumetric coherence available from adjacent images. We newly adopted a recurrent neural network, bi-directional convolutional long short-term memory (Bi-CLSTM) architecture, which can learn coherence between a sequence of data. This coherence learning capability can be useful for challenging situations, for example, when the target image exhibits inherent postoperative artifacts and noises, the inclusion of adjacent images would facilitate learning more robust features for thrombus ROI segmentation. We demonstrate the segmentation capability of our Bi-CLSTM-based method with a comparison of the existing 2D-based thrombus ROI segmentation counterpart as well as other established 2D- and 3D-based alternatives. Our comparison is based on a large-scale clinical dataset of 60 patient studies (i.e., 60 CTA image volumes). The results suggest the superior segmentation performance of our Bi–CLSTM-based method by achieving the highest scores of the evaluation metrics, e.g., our Bi-CLSTM results were 0.0331 higher on total overlap and 0.0331 lower on false negative when compared to 2D U-net++ as the second-best.

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Artificial Intelligence Application to Screen Abdominal Aortic Aneurysm Using Computed tomography Angiography

et al. 2023

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The aim of our study is to validate a totally automated deep learning (DL)-based segmentation pipeline to screen abdominal aortic aneurysms (AAA) in computed tomography angiography (CTA) scans. We retrospectively evaluated 73 thoraco-abdominal CTAs (48 AAA and 25 control CTA) by means of a DL-based segmentation pipeline built on a 2.5D convolutional neural network (CNN) architecture to segment lumen and thrombus of the aorta. The maximum aortic diameter of the abdominal tract was compared using a threshold value (30 mm). Blinded manual measurements from a radiologist were done in order to create a true comparison. The screening pipeline was tested on 48 patients with aneurysm and 25 without aneurysm. The average diameter manually measured was 51.1 ± 14.4 mm for patients with aneurysms and 21.7 ± 3.6 mm for patients without aneurysms. The pipeline correctly classified 47 AAA out of 48 and 24 control patients out of 25 with 97% accuracy, 98% sensitivity, and 96% specificity. The automated pipeline of aneurysm measurements in the abdominal tract reported a median error with regard to the maximum abdominal diameter measurement of 1.3 mm. Our approach allowed for the maximum diameter of 51.2 ± 14.3 mm in patients with aneurysm and 22.0 ± 4.0 mm in patients without an aneurysm. The DL-based screening for AAA is a feasible and accurate method, calling for further validation using a larger pool of diagnostic images towards its clinical use.

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Automatic Detection and Segmentation of Thrombi in Abdominal Aortic Aneurysms Using a Mask Region-Based Convolutional Neural Network with Optimized Loss Functions

Cited by 17 publications

References 50 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

Abdominal Aortic Thrombus Segmentation in Postoperative Computed Tomography Angiography Images Using Bi-Directional Convolutional Long Short-Term Memory Architecture

Artificial Intelligence Application to Screen Abdominal Aortic Aneurysm Using Computed tomography Angiography

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