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Abdolmanafi A, Forneris A, Moore RD, Di Martino ES. Deep-learning method for fully automatic segmentation of the abdominal aortic aneurysm from computed tomography imaging. Front Cardiovasc Med 2023; 9:1040053. [PMID: 36684599 PMCID: PMC9849751 DOI: 10.3389/fcvm.2022.1040053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Accepted: 11/28/2022] [Indexed: 01/07/2023] Open
Abstract
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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Affiliation(s)
| | - Arianna Forneris
- R&D Department, ViTAA Medical Solutions, Montreal, QC, Canada
- Department of Biomedical Engineering, Schulich School of Engineering, University of Calgary, Calgary, AB, Canada
| | - Randy D. Moore
- R&D Department, ViTAA Medical Solutions, Montreal, QC, Canada
- Division of Vascular Surgery, University of Calgary, Calgary, AB, Canada
| | - Elena S. Di Martino
- R&D Department, ViTAA Medical Solutions, Montreal, QC, Canada
- Department of Biomedical Engineering, Schulich School of Engineering, University of Calgary, Calgary, AB, Canada
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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. SENSORS 2022; 22:s22103643. [PMID: 35632050 PMCID: PMC9145191 DOI: 10.3390/s22103643] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Revised: 04/27/2022] [Accepted: 05/09/2022] [Indexed: 11/28/2022]
Abstract
The detection and segmentation of thrombi are essential for monitoring the disease progression of abdominal aortic aneurysms (AAAs) and for patient care and management. As they have inherent capabilities to learn complex features, deep convolutional neural networks (CNNs) have been recently introduced to improve thrombus detection and segmentation. However, investigations into the use of CNN methods is in the early stages and most of the existing methods are heavily concerned with the segmentation of thrombi, which only works after they have been detected. In this work, we propose a fully automated method for the whole process of the detection and segmentation of thrombi, which is based on a well-established mask region-based convolutional neural network (Mask R-CNN) framework that we improve with optimized loss functions. The combined use of complete intersection over union (CIoU) and smooth L1 loss was designed for accurate thrombus detection and then thrombus segmentation was improved with a modified focal loss. We evaluated our method against 60 clinically approved patient studies (i.e., computed tomography angiography (CTA) image volume data) by conducting 4-fold cross-validation. The results of comparisons to multiple other state-of-the-art methods suggested the superior performance of our method, which achieved the highest F1 score for thrombus detection (0.9197) and outperformed most metrics for thrombus segmentation.
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Automated Segmentation of the Human Abdominal Vascular System Using a Hybrid Approach Combining Expert System and Supervised Deep Learning. J Clin Med 2021; 10:jcm10153347. [PMID: 34362129 PMCID: PMC8347188 DOI: 10.3390/jcm10153347] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2021] [Revised: 07/25/2021] [Accepted: 07/27/2021] [Indexed: 11/16/2022] Open
Abstract
Background: Computed tomography angiography (CTA) is one of the most commonly used imaging technique for the management of vascular diseases. Here, we aimed to develop a hybrid method combining a feature-based expert system with a supervised deep learning (DL) algorithm to enable a fully automatic segmentation of the abdominal vascular tree. Methods: We proposed an algorithm based on the hybridization of a data-driven convolutional neural network and a knowledge-based model dedicated to vascular system segmentation. By using two distinct datasets of CTA from patients to evaluate independence to training dataset, the accuracy of the hybrid method for lumen and thrombus segmentation was evaluated compared to the feature-based expert system alone and to the ground truth provided by a human expert. Results: The hybrid approach demonstrated a better accuracy for lumen segmentation compared to the expert system alone (volume similarity: 0.8128 vs. 0.7912, p = 0.0006 and Dice similarity coefficient: 0.8266 vs. 0.7942, p < 0.0001). The accuracy for thrombus segmentation was also enhanced using the hybrid approach (volume similarity: 0.9404 vs. 0.9185, p = 0.0027 and Dice similarity coefficient: 0.8918 vs. 0.8654, p < 0.0001). Conclusions: By enabling a robust and fully automatic segmentation, the method could be used to develop real-time decision support to help in the management of vascular diseases.
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Computer-aided quantification of non-contrast 3D black blood MRI as an efficient alternative to reference standard manual CT angiography measurements of abdominal aortic aneurysms. Eur J Radiol 2020; 134:109396. [PMID: 33217686 DOI: 10.1016/j.ejrad.2020.109396] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2020] [Revised: 10/12/2020] [Accepted: 11/02/2020] [Indexed: 11/20/2022]
Abstract
BACKGROUND Non-contrast 3D black blood MRI is a promising tool for abdominal aortic aneurysm (AAA) surveillance, permitting accurate aneurysm diameter measurements needed for patient management. PURPOSE To evaluate whether automated AAA volume and diameter measurements obtained from computer-aided segmentation of non-contrast 3D black blood MRI are accurate, and whether they can supplant reference standard manual measurements from contrast-enhanced CT angiography (CTA). MATERIALS AND METHODS Thirty AAA patients (mean age, 71.9 ± 7.9 years) were recruited between 2014 and 2017. Participants underwent both non-contrast black blood MRI and CTA within 3 months of each other. Semi-automatic (computer-aided) MRI and CTA segmentations utilizing deformable registration methods were compared against manual segmentations of the same modality using the Dice similarity coefficient (DSC). AAA lumen and total aneurysm volumes and AAA maximum diameter, quantified automatically from these segmentations, were compared against manual measurements using Pearson correlation and Bland-Altman analyses. Finally, automated measurements from non-contrast 3D black blood MRI were evaluated against manual CTA measurements using the Wilcoxon test, Pearson correlation and Bland-Altman analyses. RESULTS Semi-automatic segmentations had excellent agreement with manual segmentations (lumen DSC: 0.91 ± 0.03 and 0.94 ± 0.03; total aneurysm DSC: 0.92 ± 0.02 and 0.94 ± 0.03, for black blood MRI and CTA, respectively). Automated volume and maximum diameter measurements also had excellent correlation to their manual counterparts for both black blood MRI (volume: r = 0.99, P < 0.001; diameter: r = 0.97, P < 0.001) and CTA (volume: r = 0.99, P < 0.001; diameter: r = 0.97, P < 0.001). Compared to manual CTA measurements, bias and limits of agreement (LOA) for automated MRI measurements (lumen volume: 1.49, [-4.19 7.17] cm3; outer wall volume: -2.46, [-14.05 9.13] cm3; maximal diameter: 0.08, [-6.51 6.67] mm) were largely equivalent to those of manual MRI measurements, particularly for maximum AAA diameter (lumen volume: 0.73, [-6.47 7.93] cm3; outer wall volume: 0.98, [-10.54 12.5] cm3; maximal diameter: 0.08, [-3.67 3.83] mm). CONCLUSION Semi-automatic segmentation of non-contrast 3D black blood MRI efficiently provides reproducible morphologic AAA assessment yielding accurate AAA diameters and volumes with no clinically relevant differences compared to either automatic or manual measurements based on CTA.
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Kaschwich M, Horn M, Matthiensen S, Stahlberg E, Behrendt CA, Matysiak F, Bouchagiar J, Dell A, Ellebrecht D, Bayer A, Kleemann M. Accuracy evaluation of patient-specific 3D-printed aortic anatomy. Ann Anat 2020; 234:151629. [PMID: 33137459 DOI: 10.1016/j.aanat.2020.151629] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Revised: 10/07/2020] [Accepted: 10/08/2020] [Indexed: 11/20/2022]
Abstract
INTRODUCTION 3D printing has a wide range of applications in medicine. In surgery, this technique can be used for preoperative planning of complex procedures, production of patient specific implants, as well as training. However, accuracy evaluations of 3D vascular models are rare. OBJECTIVES Aim of this study was to investigate the accuracy of patient-specific 3D-printed aortic anatomies. METHODS Patients suffering from aorto-iliac aneurysms and with indication for treatment were selected on the basis of different anatomy and localization of the aneurysm in the period from January 1st 2014 to May 27th 2016. Six patients with aorto-iliac aneurysms were selected out of the database for 3D-printing. Subsequently, computed tomography (CT) images of the printed 3D-models were compared with the original CT data sets. RESULTS The mean deviation of the six 3D-vascular models ranged between -0.73 mm and 0.14 mm compared to the original CT-data. The relative deviation of the measured values showed no significant difference between the 3D-vascular and the original patient CT-data. CONCLUSION Our results showed that 3D printing has the potential to produce patient-specific 3D vascular models with reliable accuracy. This enables the use of such models for the development of new endovascular procedures and devices.
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Affiliation(s)
- Mark Kaschwich
- Biomedical Engineering Laboratory, University Medical Center Schleswig-Holstein, Campus Lübeck, Ratzeburger Allee 160, 23538 Lübeck, Germany; Department of Vascular Medicine, University Heart and Vascular Center Hamburg, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany.
| | - Marco Horn
- Department of Surgery, Division of Vascular and Endovascular Surgery, University Medical Center Schleswig-Holstein, Campus Lübeck, Ratzeburger Allee 160, 23538 Lübeck, Germany
| | - Sarah Matthiensen
- Biomedical Engineering Laboratory, University Medical Center Schleswig-Holstein, Campus Lübeck, Ratzeburger Allee 160, 23538 Lübeck, Germany
| | - Erik Stahlberg
- Department for Radiology and Nuclear Medicine, University Hospital of Schleswig-Holstein, Campus Lübeck, Germany
| | - Christian-Alexander Behrendt
- Department of Vascular Medicine, University Heart and Vascular Center Hamburg, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany
| | - Florian Matysiak
- Biomedical Engineering Laboratory, University Medical Center Schleswig-Holstein, Campus Lübeck, Ratzeburger Allee 160, 23538 Lübeck, Germany
| | - Juljan Bouchagiar
- Biomedical Engineering Laboratory, University Medical Center Schleswig-Holstein, Campus Lübeck, Ratzeburger Allee 160, 23538 Lübeck, Germany
| | - Annika Dell
- Biomedical Engineering Laboratory, University Medical Center Schleswig-Holstein, Campus Lübeck, Ratzeburger Allee 160, 23538 Lübeck, Germany
| | | | - Andreas Bayer
- Institute of Anatomy, Christian-Albrechts University of Kiel, Kiel, Germany
| | - Markus Kleemann
- Biomedical Engineering Laboratory, University Medical Center Schleswig-Holstein, Campus Lübeck, Ratzeburger Allee 160, 23538 Lübeck, Germany; Kliniken Dr. Erler, 90429 Nürnberg, Germany
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Siriapisith T, Kusakunniran W, Haddawy P. Pyramid graph cut: Integrating intensity and gradient information for grayscale medical image segmentation. Comput Biol Med 2020; 126:103997. [PMID: 32987203 DOI: 10.1016/j.compbiomed.2020.103997] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Revised: 08/30/2020] [Accepted: 08/30/2020] [Indexed: 11/17/2022]
Abstract
Segmentation of grayscale medical images is challenging because of the similarity of pixel intensities and poor gradient strength between adjacent regions. The existing image segmentation approaches based on either intensity or gradient information alone often fail to produce accurate segmentation results. Previous approaches in the literature have approached the problem by embedded or sequential integration of different information types to improve the performance of the image segmentation on specific tasks. However, an effective combination or integration of such information is difficult to implement and not sufficiently generic for closely related tasks. Integration of the two information sources in a single graph structure is a potentially more effective way to solve the problem. In this paper we introduce a novel technique for grayscale medical image segmentation called pyramid graph cut, which combines intensity and gradient sources of information in a pyramid-shaped graph structure using a single source node and multiple sink nodes. The source node, which is the top of the pyramid graph, embeds intensity information into its linked edges. The sink nodes, which are the base of the pyramid graph, embed gradient information into their linked edges. The min-cut uses intensity information and gradient information, depending on which one is more useful or has a higher influence in each cutting location of each iteration. The experimental results demonstrate the effectiveness of the proposed method over intensity-based segmentation alone (i.e. Gaussian mixture model) and gradient-based segmentation alone (i.e. distance regularized level set evolution) on grayscale medical image datasets, including the public 3DIRCADb-01 dataset. The proposed method archives excellent segmentation results on the sample CT of abdominal aortic aneurysm, MRI of liver tumor and US of liver tumor, with dice scores of 90.49±5.23%, 88.86±11.77%, 90.68±2.45%, respectively.
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Affiliation(s)
- Thanongchai Siriapisith
- Department Radiology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, 10700, Thailand.
| | - Worapan Kusakunniran
- Faculty of Information and Communication Technology, Mahidol University, Nakhonpathom, 73170, Thailand
| | - Peter Haddawy
- Faculty of Information and Communication Technology, Mahidol University, Nakhonpathom, 73170, Thailand; Bremen Spatial Cognition Center, University of Bremen, Bremen, Germany
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Abdominal Aortic Aneurysm Segmentation Using Convolutional Neural Networks Trained with Images Generated with a Synthetic Shape Model. ACTA ACUST UNITED AC 2019. [PMID: 34113925 DOI: 10.1007/978-3-030-33327-0_20] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register]
Abstract
An abdominal aortic aneurysm (AAA) is a ballooning of the abdominal aorta, that if not treated tends to grow and rupture. Computed Tomography Angiography (CTA) is the main imaging modality for the management of AAAs, and segmenting them is essential for AAA rupture risk and disease progression assessment. Previous works have shown that Convolutional Neural Networks (CNNs) can accurately segment AAAs, but have the limitation of requiring large amounts of annotated data to train the networks. Thus, in this work we propose a methodology to train a CNN only with images generated with a synthetic shape model, and test its generalization and ability to segment AAAs from new original CTA scans. The synthetic images are created from realistic deformations generated by applying principal component analysis to the deformation fields obtained from the registration of few datasets. The results show that the performance of a CNN trained with synthetic data to segment AAAs from new scans is comparable to the one of a network trained with real images. This suggests that the proposed methodology may be applied to generate images and train a CNN to segment other types of aneurysms, reducing the burden of obtaining large annotated image databases.
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Siriapisith T, Kusakunniran W, Haddawy P. 3D segmentation of exterior wall surface of abdominal aortic aneurysm from CT images using variable neighborhood search. Comput Biol Med 2019; 107:73-85. [PMID: 30782525 DOI: 10.1016/j.compbiomed.2019.01.027] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2018] [Revised: 01/15/2019] [Accepted: 01/30/2019] [Indexed: 11/18/2022]
Abstract
A 3D model of abdominal aortic aneurysm (AAA) can provide useful anatomical information for clinical management and simulation. Thin-slice contiguous computed tomographic (CT) angiography is the best source of medical images for construction of 3D models, which requires segmentation of AAA in the images. Existing methods for segmentation of AAA rely on either manual process or 2D segmentation in each 2D CT slide. However, a traditional manual segmentation is a time consuming process which is not practical for routine use. The construction of a 3D model from 2D segmentation of each CT slice is not a fully satisfactory solution due to rough contours that can occur because of lack of constraints among segmented slices, as well as missed segmentation slices. To overcome such challenges, this paper proposes the 3D segmentation of AAA using the concept of variable neighborhood search by iteratively alternating between two different segmentation techniques in the two different 3D search spaces of voxel intensity and voxel gradient. The segmentation output of each method is used as the initial contour to the other method in each iteration. By alternating between search spaces, the technique can escape local minima that naturally occur in each search space. Also, the 3D search spaces provide more constraints across CT slices, when compared with the 2D search spaces in individual CT slices. The proposed method is evaluated with 10 easy and 10 difficult cases of AAA. The results show that the proposed 3D segmentation technique achieves the outstanding segmentation accuracy with an average dice similarity value (DSC) of 91.88%, when compared to the other methods using the same dataset, which are the 2D proposed method, classical graph cut, distance regularized level set evolution, and registration based geometric active contour with the DSCs of 87.57 ± 4.52%, 72.47 ± 8.11%, 58.50 ± 8.86% and 76.21 ± 10.49%, respectively.
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Affiliation(s)
- Thanongchai Siriapisith
- Faculty of Information and Communication Technology, Mahidol University, Nakhonpathom, Thailand; Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Worapan Kusakunniran
- Faculty of Information and Communication Technology, Mahidol University, Nakhonpathom, Thailand.
| | - Peter Haddawy
- Faculty of Information and Communication Technology, Mahidol University, Nakhonpathom, Thailand; Bremen Spatial Cognition Center, University of Bremen, Bremen, Germany
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