Abstract
Type-B Aortic Dissection (TBAD) is one of the most serious cardiovascular events characterized by a growing yearly incidence, and the severity of disease prognosis. Currently, computed tomography angiography (CTA) has been widely adopted for the diagnosis and prognosis of TBAD. Accurate segmentation of true lumen (TL), false lumen (FL), and false lumen thrombus (FLT) in CTA are crucial for the precise quantification of anatomical features. However, existing works only focus on only TL and FL without considering FLT. In this paper, we propose ImageTBAD, the first 3D computed tomography angiography (CTA) image dataset of TBAD with annotation of TL, FL, and FLT. The proposed dataset contains 100 TBAD CTA images, which is of decent size compared with existing medical imaging datasets. As FLT can appear almost anywhere along the aorta with irregular shapes, segmentation of FLT presents a wide class of segmentation problems where targets exist in a variety of positions with irregular shapes. We further propose a baseline method for automatic segmentation of TBAD. Results show that the baseline method can achieve comparable results with existing works on aorta and TL segmentation. However, the segmentation accuracy of FLT is only 52%, which leaves large room for improvement and also shows the challenge of our dataset. To facilitate further research on this challenging problem, our dataset and codes are released to the public (Dataset, 2020).
Highlights
Type-B aortic dissection (TBAD) is the surging of blood through a tear in the aortic intima with separation of the intima and media, and creation of a false lumen as shown in Figure 1, which is one of the most serious cardiovascular events
false lumen thrombus (FLT) segmentation represents a wide class of segmentation problems where targets exist in a variety of positions with irregular shapes
Results show that the baseline method can achieve comparable results with existing works on the aorta and true lumen (TL) segmentation
Summary
Type-B aortic dissection (TBAD) is the surging of blood through a tear in the aortic intima with separation of the intima and media, and creation of a false lumen (channel) as shown in Figure 1, which is one of the most serious cardiovascular events. Li et al report a fully automatic approach based on a 3-D multi-task deep convolutional neural network that segments the entire aorta and true-false lumen from CTA images in a unified framework. Cao et al use a convolutional neural network to solve the problems and achieves above 90% of the mean Dice coefficients of each lumen of TBAD when not considering FLT. They provide a promising approach for accurate and efficient segmentation of TBAD and make it possible for automated measurements of TBAD anatomical features. To facilitate further research on this challenging topic, our dataset and codes are released to the public (Dataset, 2020)
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