PurposeDiagnosis and surveillance of thoracic aortic aneurysm (TAA) involves measuring the aortic diameter at various locations along the length of the aorta, often using computed tomography angiography (CTA). Currently, measurements are performed by human raters using specialized software for three-dimensional analysis, a time-consuming process, requiring 15 to 45 min of focused effort. Thus, we aimed to develop a convolutional neural network (CNN)-based algorithm for fully automated and accurate aortic measurements.ApproachUsing 212 CTA scans, we trained a CNN to perform segmentation and localization of key landmarks jointly. Segmentation mask and landmarks are subsequently used to obtain the centerline and cross-sectional diameters of the aorta. Subsequently, a cubic spline is fit to the aortic boundary at the sinuses of Valsalva to avoid errors related inclusions of coronary artery origins. Performance was evaluated on a test set of 60 scans with automated measurements compared against expert manual raters.ResultCompared to training separate networks for each task, joint training yielded higher accuracy for segmentation, especially at the boundary (p < 0.001), but a marginally worse (0.2 to 0.5 mm) accuracy for landmark localization (p < 0.001). Mean absolute error between human and automated was ≤1 mm at six of nine standard clinical measurement locations. However, higher errors were noted in the aortic root and arch regions, ranging between 1.4 and 2.2 mm, although agreement of manual raters was also lower in these regions.ConclusionFully automated aortic diameter measurements in TAA are feasible using a CNN-based algorithm. Automated measurements demonstrated low errors that are comparable in magnitude to those with manual raters; however, measurement error was highest in the aortic root and arch.
Diagnosis of thoracic aortic aneurysm typically involves measuring the diameters at various locations on the aorta from computed tomography angiograms (CTAs). Human measurement is time-consuming and suffers from inter and intra-user variability, motivating the need for automated, repeatable measurement software. This work presents a convolutional neural network (CNN)-based algorithm for fully automated aortic measurements. We employ the CNN to perform aortic segmentation and localization of key landmarks jointly, which performs better than individual models for each task. The segmentation mask and landmarks are subsequently used to obtain the centerline and cross-sectional diameters of the aorta using a combination of image processing techniques. We gather a dataset of CTAs from patients with ongoing imaging surveillance of thoracic aortic aneurysm and demonstrate the performance of our algorithm by quantitative comparisons against measurements from human raters. We observe that for most locations, the mean absolute error between human and computer-generated measurements is less than 1 mm, which is at or lower than the level of variability in human measurements. Furthermore, we showcase the behavior of our method through various visual examples, discuss its limitations and propose possible improvements.
Landmark detection is a critical component of the image processing pipeline for automated aortic size measurements. Given that the thoracic aorta has a relatively conserved topology across the population and that a human annotator with minimal training can estimate the location of unseen landmarks from limited examples, we proposed an auxiliary learning task to learn the implicit topology of aortic landmarks through a CNN-based network. Specifically, we created a network to predict the location of missing landmarks from the visible ones by minimizing the Implicit Topology loss in an end-to-end manner. The proposed learning task can be easily adapted and combined with Unet-style backbones. To validate our method, we utilized a dataset consisting of 207 CTAs, labeling four landmarks on each aorta. Our method outperforms the state-of-the-art Unet-style architectures (ResUnet, UnetR) in terms of localization accuracy, with only a light (#params=0.4M) overhead. We also demonstrate our approach in two clinically meaningful applications: aortic sub-region division and automatic centerline generation.
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