SeqSeg: Learning Local Segments for Automatic Vascular Model Construction

TL;DR

SeqSeg introduces a deep learning method for automatic vascular segmentation that improves completeness and generalization over existing models, streamlining cardiovascular model construction from medical images.

Contribution

The paper presents SeqSeg, a novel sequential local segmentation approach using U-Net, enhancing vascular model accuracy and generalization in medical image analysis.

Findings

SeqSeg outperforms benchmark models in segmenting complete vasculature.

SeqSeg generalizes well to unannotated vascular structures.

SeqSeg is effective on both CT and MR images.

Abstract

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.