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A deep learning approach to automate high-resolution blood vessel reconstruction on computerised tomography images with or without the use of contrast agents

Session Computed Tomography ePosters

Speaker Anirudh Chandrashekar

Event : ESC Congress 2020

  • Topic : imaging
  • Sub-topic : Computed Tomography: Technology
  • Session type : ePosters

Authors : AC Chandrashekar (Oxford,GB), AH Handa (Oxford,GB), NS Shivakumar (Oxford,GB), PL Lapolla (Oxford,GB), VG Grau (Oxford,GB), RL Lee (Oxford,GB)

AC Chandrashekar1 , AH Handa1 , NS Shivakumar1 , PL Lapolla1 , VG Grau2 , RL Lee1 , 1University of Oxford, Nuffield Department of Surgical Sciences - Oxford - United Kingdom of Great Britain & Northern Ireland , 2University of Oxford, Department of Engineering Science - Oxford - United Kingdom of Great Britain & Northern Ireland ,

On behalf: Oxford Abdominal Aortic Aneurysm (OxAAA) Study


Existing methods to reconstruct vascular structures from a computed tomography (CT) angiogram rely on injection of intravenous contrast to enhance the radio-density within the vessel lumen. Pathological changes present within the blood lumen, vessel wall or a combination of both prevent accurate 3D reconstruction. In the example of aortic aneurysmal (AAA) disease, a blood clot or thrombus adherent to the aortic wall within the expanding aneurysmal sac is present in 95% of cases. These deformations prevent the automatic extraction of vital clinically relevant information by current methods.

In this study, we utilised deep learning segmentation methods to establish a high-throughput and automated segmentation pipeline for pathological blood vessels (ex. Aortic Aneurysm) in CT images acquired with or without the use of a contrast agent.

Twenty-six patients with paired non-contrast and contrast-enhanced CT images were randomly selected from an ethically-approved ongoing study (Ethics Ref 13/SC/0250), manually annotated and used for model training and evaluation (13/13). Data augmentation methods were implemented to diversify the training data set in a ratio of 10:1. We utilised a 3D U-Net with attention gating for both the aortic region-of-interest (ROI) detection and segmentation tasks. Trained architectures were evaluated using the DICE similarity score.

Inter- and Intra- observer analysis supports the accuracy of the manual segmentations used for model training (intra-class correlation coefficient, ‘ICC’ = 0.995 and 1.00, respective. P<0.001 for both). The performance of our Attention-based U-Net (DICE score: 94.8 ± 0.5 %) in extracting both the inner lumen and the outer wall of the aortic aneurysm from CT angiograms (CTA) was compared against a generic 3-D U-Net (DICE score: 89.5 ± 0.6 %) and displayed superior results (p < 0.01). Fig 1A depicts the implementation of this network architecture within the aortic segmentation pipeline (automated ROI detection and aortic segmentation). This pipeline has allowed accurate and efficient extraction of the entire aortic volume from both contrast-enhanced CTA (DICE score: 95.3 ± 0.6 %) and non-contrast CT (DICE score: 93.2 ± 0.7 %) images. Fig 1B illustrates the model output alongside the labelled ground truth segmentation for the pathological aneurysmal region; only minor differences are visually discernible (coloured boxes).

We developed a novel automated pipeline for high resolution reconstruction of blood vessels using deep learning approaches. This pipeline enables automatic extraction of morphologic features of blood vessels and can be applied for research and potentially for clinical use.

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