Abstract
Abstract Deep learning methods produce promising results when applied to a wide range of medical imaging tasks, including segmentation of artery lumen in computed tomography angiography (CTA) data. However, to perform sufficiently, neural networks have to be trained on large amounts of high quality annotated data. In the realm of medical imaging, annotations are not only quite scarce but also often not entirely reliable. To tackle both challenges, we developed a two-step approach for generating realistic synthetic CTA data for the purpose of data augmentation. In the first step moderately realistic images are generated in a purely numerical fashion. In the second step these images are improved by applying neural domain adaptation. We evaluated the impact of synthetic data on lumen segmentation via convolutional neural networks (CNNs) by comparing resulting performances. Improvements of up to 5% in terms of Dice coefficient and 20% for Hausdorff distance represent a proof of concept that the proposed augmentation procedure can be used to enhance deep learning-based segmentation for artery lumen in CTA images.
Highlights
Computed tomography angiography (CTA) is a minimally invasive imaging modality that can help physicians to achieve an accurate diagnosis regarding cardiovascular diseases
We extended the similarity analysis by multiscale structural similarity (MS-SSIM) [11], which compares contrast, luminance and structure of two corresponding images on multiple scales
We found that adding synthetic data to the training set improves the segmentation performance
Summary
Computed tomography angiography (CTA) is a minimally invasive imaging modality that can help physicians to achieve an accurate diagnosis regarding cardiovascular diseases. Deep learning methods can further improve the Data. We used data available through the Rotterdam Coronary Artery Algorithm Evaluation Framework (RCAAEF) [3]. It comprises 78 artery segments from 18 distinct patients, acquired using three different scanners and annotated by three different observers. Seemann et al.: Data augmentation for CTA using neural domain adaptation provided artery centerlines. This representation was found to be preferable for U-Net segmentation [5] of tubular structures. The volumes were normalized such that the grayvalues ranged between 0 and 255
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