# Fully automatic detection and segmentation of abdominal aortic thrombus in post-operative CTA images using deep convolutional neural networks

arXiv: https://arxiv.org/abs/1804.00304 · HTML: https://mlsanity.com/paper/1804.00304/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Apr 2018.

## Built on by (most cited first, 8 of 8)

- [CLIP in Medical Imaging: A Survey](https://mlsanity.com/paper/2312.07353/) (2023)
- [Attri-VAE: attribute-based interpretable representations of medical images with variational autoencoders](https://mlsanity.com/paper/2203.10417/) (2022)
- [ImageTBAD: A 3D Computed Tomography Angiography Image Dataset for Automatic Segmentation of Type-B Aortic Dissection](https://mlsanity.com/paper/2109.00374/) (2021)
- [AI-based Aortic Vessel Tree Segmentation for Cardiovascular Diseases Treatment: Status Quo](https://mlsanity.com/paper/2108.02998/) (2021)
- [CACTUSS: Common Anatomical CT-US Space for US examinations](https://mlsanity.com/paper/2207.08619/) (2022)
- [Automatic Aorta Segmentation with Heavily Augmented, High-Resolution 3-D ResUNet: Contribution to the SEG.A Challenge](https://mlsanity.com/paper/2310.15827/) (2023)
- [Coordinated Transformer with Position & Sample-aware Central Loss for Anatomical Landmark Detection](https://mlsanity.com/paper/2305.11338/) (2023)
- [Trust the Prior (or Not): Uncertainty-Aware Abdominal Aortic Aneurysm Segmentation](https://mlsanity.com/paper/2607.00201/) (2026)

## Abstract (arXiv)

Computerized Tomography Angiography (CTA) based follow-up of Abdominal Aortic Aneurysms (AAA) treated with Endovascular Aneurysm Repair (EVAR) is essential to evaluate the progress of the patient and detect complications. In this context, accurate quantification of post-operative thrombus volume is required. However, a proper evaluation is hindered by the lack of automatic, robust and reproducible thrombus segmentation algorithms. We propose a new fully automatic approach based on Deep Convolutional Neural Networks (DCNN) for robust and reproducible thrombus region of interest detection and subsequent fine thrombus segmentation. The DetecNet detection network is adapted to perform region of interest extraction from a complete CTA and a new segmentation network architecture, based on Fully Convolutional Networks and a Holistically-Nested Edge Detection Network, is presented. These networks are trained, validated and tested in 13 post-operative CTA volumes of different patients using a 4-fold cross-validation approach to provide more robustness to the results. Our pipeline achieves a Dice score of more than 82% for post-operative thrombus segmentation and provides a mean relative volume difference between ground truth and automatic segmentation that lays within the experienced human observer variance without the need of human intervention in most common cases.