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3D Pulmonary Artery Segmentation from CTA Scans Using Deep Learning with Realistic Data Augmentation.
Karen López-Linares Román1,2,3, Isaac de La Bruere4, Jorge Onieva3
1Vicomtech Foundation and Biodonostia, San Sebastián, Spain.
Image Analysis for Moving Organ, Breast, and Thoracic Images : Third International Workshop, RAMBO 2018, Fourth International Workshop, BIA 2018, and First International Workshop, TIA 2018, Held in Conjunction with MICCAI 2018, Granada
|June 5, 2020
Summary
This study introduces a novel 3D convolutional neural network for automatic pulmonary artery segmentation in CT angiography scans. This method aids in analyzing pulmonary vascular diseases and personalizing medicine.
Area of Science:
- Medical Imaging
- Artificial Intelligence in Medicine
- Cardiovascular Imaging
Background:
- Accurate characterization of mediastinal vasculature, particularly the pulmonary artery (PA), is crucial for diagnosing pulmonary vascular diseases.
- Automated segmentation of the PA in computed tomography angiography (CTA) images is needed for advanced geometrical analysis and clinical applications.
Purpose of the Study:
- To develop and validate a novel 3D convolutional neural network (CNN) for automatic segmentation of the pulmonary artery from CTA images.
- To enable detailed analysis of PA geometry in both healthy and diseased states, supporting fluid mechanics models and personalized medicine.
Main Methods:
- A new 3D CNN architecture was designed and trained on diverse patient cohorts.
- A robust data augmentation strategy using principal component analysis on deformation fields from affine registration was employed.
- The network's performance was validated on 91 datasets against semi-automatically delineated ground truths.
Main Results:
- The proposed 3D CNN achieved high accuracy in PA segmentation.
- Quantitative evaluation yielded mean Dice coefficient of 0.89, Jaccard coefficient of 0.80, and mean surface distance of 1.25 mm.
- Performance was benchmarked against a standard Unet architecture, demonstrating competitive or superior results.
Conclusions:
- The developed 3D CNN provides an effective tool for automated pulmonary artery segmentation in CTA.
- This automated approach facilitates quantitative analysis of PA morphology for clinical research and patient-specific modeling.
- The study highlights the potential of deep learning in advancing cardiovascular imaging analysis.
