# SeqSeg: Learning Local Segments for Automatic Vascular Model Construction

arXiv: https://arxiv.org/abs/2501.15712 · HTML: https://mlsanity.com/paper/2501.15712/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 24 references that are in the map.
- First posted on arXiv 27 Jan 2025.

## Builds on (most cited first, 4 of 4)

- [A Deep-Learning Approach For Direct Whole-Heart Mesh Reconstruction](https://mlsanity.com/paper/2102.07899/) (2021)
- [Coronary Artery Centerline Extraction in Cardiac CT Angiography Using a CNN-Based Orientation Classifier](https://mlsanity.com/paper/1810.03143/) (2018)
- [Quantitative Evaluation of Gender Bias in Astronomical Publications from Citation Counts](https://mlsanity.com/paper/1610.08984/) (2016)
- [Learning-Based Algorithms for Vessel Tracking: A Review](https://mlsanity.com/paper/2012.08929/) (2020)

## Built on by (most cited first, 1 of 1)

- [AI-Powered Automated Model Construction for Patient-Specific CFD Simulations of Aortic Flows](https://mlsanity.com/paper/2503.12515/) (2025)

## Abstract (arXiv)

Computational modeling of cardiovascular function has become a critical part of diagnosing, treating and understanding cardiovascular disease. Most strategies involve constructing anatomically accurate computer models of cardiovascular structures, which is a multistep, time-consuming process. To improve the model generation process, we herein present SeqSeg (sequential segmentation): a novel deep learning based automatic tracing and segmentation algorithm for constructing image-based vascular models. SeqSeg leverages local U-Net-based inference to sequentially segment vascular structures from medical image volumes. We tested SeqSeg on CT and MR images of aortic and aortofemoral models and compared the predictions to those of benchmark 2D and 3D global nnU-Net models, which have previously shown excellent accuracy for medical image segmentation. We demonstrate that SeqSeg is able to segment more complete vasculature and is able to generalize to vascular structures not annotated in the training data.