# ImageTBAD: A 3D Computed Tomography Angiography Image Dataset for Automatic Segmentation of Type-B Aortic Dissection

arXiv: https://arxiv.org/abs/2109.00374 · HTML: https://mlsanity.com/paper/2109.00374/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 17 references that are in the map.
- First posted on arXiv 1 Sep 2021.

## Builds on (most cited first, 3 of 3)

- [3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation](https://mlsanity.com/paper/1606.06650/) (2016)
- [nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation](https://mlsanity.com/paper/1809.10486/) (2018)
- [Fully automatic detection and segmentation of abdominal aortic thrombus in post-operative CTA images using deep convolutional neural networks](https://mlsanity.com/paper/1804.00304/) (2018)

## Built on by (most cited first, 14 of 14)

- [Multi-Class Segmentation of Aortic Branches and Zones in Computed Tomography Angiography: The AortaSeg24 Challenge](https://mlsanity.com/paper/2502.05330/) (2025)
- [MedSG-Bench: A Benchmark for Medical Image Sequences Grounding](https://mlsanity.com/paper/2505.11852/) (2025)
- [CIS-UNet: Multi-Class Segmentation of the Aorta in Computed Tomography Angiography via Context-Aware Shifted Window Self-Attention](https://mlsanity.com/paper/2401.13049/) (2024)
- [EPL: Evidential Prototype Learning for Semi-supervised Medical Image Segmentation](https://mlsanity.com/paper/2404.06181/) (2024)
- [Synthesizing CTA Image Data for Type-B Aortic Dissection using Stable Diffusion Models](https://mlsanity.com/paper/2402.06969/) (2024)
- [Interpretable Auto Window Setting for Deep-Learning-Based CT Analysis](https://mlsanity.com/paper/2501.06223/) (2025)
- [VesselSAM: Leveraging SAM for Aortic Vessel Segmentation with AtrousLoRA](https://mlsanity.com/paper/2502.18185/) (2025)
- [Adaptive Morph-Patch Transformer for Aortic Vessel Segmentation](https://mlsanity.com/paper/2511.06897/) (2025)
- [Deep Medial Voxels: Learned Medial Axis Approximations for Anatomical Shape Modeling](https://mlsanity.com/paper/2403.11790/) (2024)
- [Mutual Evidential Deep Learning for Medical Image Segmentation](https://mlsanity.com/paper/2505.12418/) (2025)
- [Advanced Deep Learning Techniques for Automated Segmentation of Type B Aortic Dissections](https://mlsanity.com/paper/2506.22222/) (2025)
- [Harmonizing Generalization and Specialization: Uncertainty-Informed Collaborative Learning for Semi-supervised Medical Image Segmentation](https://mlsanity.com/paper/2512.13101/) (2025)
- [Semi-Supervised 3D Segmentation for Type-B Aortic Dissection with Slim UNETR](https://mlsanity.com/paper/2512.17610/) (2025)
- [Region of interest detection for efficient aortic segmentation](https://mlsanity.com/paper/2601.08683/) (2026)

## Abstract (arXiv)

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.