# Coronary Artery Centerline Extraction in Cardiac CT Angiography Using a CNN-Based Orientation Classifier

arXiv: https://arxiv.org/abs/1810.03143 · HTML: https://mlsanity.com/paper/1810.03143/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Oct 2018.

## Built on by (most cited first, 16 of 16)

- [RTN: Reinforced Transformer Network for Coronary CT Angiography Vessel-level Image Quality Assessment](https://mlsanity.com/paper/2207.06177/) (2022)
- [Enforcing connectivity of 3D linear structures using their 2D projections](https://mlsanity.com/paper/2207.06832/) (2022)
- [ConvNeXtv2 Fusion with Mask R-CNN for Automatic Region Based Coronary Artery Stenosis Detection for Disease Diagnosis](https://mlsanity.com/paper/2310.04749/) (2023)
- [CAPE: Connectivity-Aware Path Enforcement Loss for Curvilinear Structure Delineation](https://mlsanity.com/paper/2504.00753/) (2025)
- [Deep Feature Fusion via Graph Convolutional Network for Intracranial Artery Labeling](https://mlsanity.com/paper/2205.10757/) (2022)
- [SIRE: scale-invariant, rotation-equivariant estimation of artery orientations using graph neural networks](https://mlsanity.com/paper/2311.05400/) (2023)
- [Global Control for Local SO(3)-Equivariant Scale-Invariant Vessel Segmentation](https://mlsanity.com/paper/2403.15314/) (2024)
- [SeqSeg: Learning Local Segments for Automatic Vascular Model Construction](https://mlsanity.com/paper/2501.15712/) (2025)
- [Deep Open Snake Tracker for Vessel Tracing](https://mlsanity.com/paper/2107.09049/) (2021)
- [Disarranged Zone Learning (DZL): An unsupervised and dynamic automatic stenosis recognition methodology based on coronary angiography](https://mlsanity.com/paper/2110.00896/) (2021)
- [Orientation recognition and correction of Cardiac MRI with deep neural network](https://mlsanity.com/paper/2211.11336/) (2022)
- [O2CTA: Introducing Annotations from OCT to CCTA in Coronary Plaque Analysis](https://mlsanity.com/paper/2303.06358/) (2023)
- [Cardiac MRI Orientation Recognition and Standardization using Deep Neural Networks](https://mlsanity.com/paper/2308.00615/) (2023)
- [An automated framework for brain vessel centerline extraction from CTA images](https://mlsanity.com/paper/2401.07041/) (2024)
- [ViG3D-UNet: Volumetric Vascular Connectivity-Aware Segmentation via 3D Vision Graph Representation](https://mlsanity.com/paper/2504.13599/) (2025)
- [VesselFusion: Diffusion Models for Vessel Centerline Extraction from 3D CT Images](https://mlsanity.com/paper/2603.08135/) (2026)

## Abstract (arXiv)

Coronary artery centerline extraction in cardiac CT angiography (CCTA) images is a prerequisite for evaluation of stenoses and atherosclerotic plaque. We propose an algorithm that extracts coronary artery centerlines in CCTA using a convolutional neural network (CNN). A 3D dilated CNN is trained to predict the most likely direction and radius of an artery at any given point in a CCTA image based on a local image patch. Starting from a single seed point placed manually or automatically anywhere in a coronary artery, a tracker follows the vessel centerline in two directions using the predictions of the CNN. Tracking is terminated when no direction can be identified with high certainty. The CNN was trained using 32 manually annotated centerlines in a training set consisting of 8 CCTA images provided in the MICCAI 2008 Coronary Artery Tracking Challenge (CAT08). Evaluation using 24 test images of the CAT08 challenge showed that extracted centerlines had an average overlap of 93.7% with 96 manually annotated reference centerlines. Extracted centerline points were highly accurate, with an average distance of 0.21 mm to reference centerline points. In a second test set consisting of 50 CCTA scans, 5,448 markers in the coronary arteries were used as seed points to extract single centerlines. This showed strong correspondence between extracted centerlines and manually placed markers. In a third test set containing 36 CCTA scans, fully automatic seeding and centerline extraction led to extraction of on average 92% of clinically relevant coronary artery segments. The proposed method is able to accurately and efficiently determine the direction and radius of coronary arteries. The method can be trained with limited training data, and once trained allows fast automatic or interactive extraction of coronary artery trees from CCTA images.