mlsanityData to 7 Oct 2026

Robust Implementation of Foreground Extraction and Vessel Segmentation for X-ray Coronary Angiography Image Sequence

arXiv 2209.07237 ↗First posted 15 Sep 2022cs.CVcited by 4

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Abstract (arXiv)

The extraction of contrast-filled vessels from X-ray coronary angiography (XCA) image sequence has important clinical significance for intuitively diagnosis and therapy. In this study, the XCA image sequence is regarded as a 3D tensor input, the vessel layer is regarded as a sparse tensor, and the background layer is regarded as a low-rank tensor. Using tensor nuclear norm (TNN) minimization, a novel method for vessel layer extraction based on tensor robust principal component analysis (TRPCA) is proposed. Furthermore, considering the irregular movement of vessels and the low-frequency dynamic disturbance of surrounding irrelevant tissues, the total variation (TV) regularized spatial-temporal constraint is introduced to smooth the foreground layer. Subsequently, for vessel layer images with uneven contrast distribution, a two-stage region growing (TSRG) method is utilized for vessel enhancement and segmentation. A global threshold method is used as the preprocessing to obtain main branches, and the Radon-Like features (RLF) filter is used to enhance and connect broken minor segments, the final binary vessel mask is constructed by combining the two intermediate results. The visibility of TV-TRPCA algorithm for foreground extraction is evaluated on clinical XCA image sequences and third-party dataset, which can effectively improve the performance of commonly used vessel segmentation algorithms. Based on TV-TRPCA, the accuracy of TSRG algorithm for vessel segmentation is further evaluated. Both qualitative and quantitative results validate the superiority of the proposed method over existing state-of-the-art approaches.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 17 of its 92 references that are in the map. First posted on arXiv 15 Sep 2022.

Builds on

17 of 92 references.

References · 17

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  2. 2014Fully Convolutional Networks for Semantic Segmentationcited by 4,845
  3. 2018Tensor Robust Principal Component Analysis with A New Tensor Nuclear Normcited by 110
  4. 2017Hyperspectral Image Restoration via Total Variation Regularized Low-rank Tensor Decompositioncited by 61
  5. 2014Novel methods for multilinear data completion and de-noising based on tensor-SVDcited by 56
  6. 2018Deep Vessel Segmentation By Learning Graphical Connectivitycited by 43
  7. 2017Tensor Robust Principal Component Analysis: Exact Recovery of Corrupted Low-Rank Tensors via Convex Optimizationcited by 35
  8. 2013Robust Low-rank Tensor Recovery: Models and Algorithmscited by 33
  9. 2015Weighted Schatten p-Norm Minimization for Image Denoising and Background Subtractioncited by 29
  10. 2017Accelerated Alternating Projections for Robust Principal Component Analysiscited by 25
  11. 2015Total Variation Regularized Tensor RPCA for Background Subtraction from Compressive Measurementscited by 14
  12. 2020Weakly Supervised Vessel Segmentation in X-ray Angiograms by Self-Paced Learning from Noisy Labels with Suggestive Annotationcited by 14
5 more
  1. 2021Sequential vessel segmentation via deep channel attention networkcited by 14
  2. 2018Deep Segmentation and Registration in X-Ray Angiography Videocited by 8
  3. 2017Panoramic Robust PCA for Foreground-Background Separation on Noisy, Free-Motion Camera Videocited by 6
  4. 2019Tensor Robust Principal Component Analysis: Better recovery with atomic norm regularizationcited by 5
  5. 2022Robust PCA Unrolling Network for Super-resolution Vessel Extraction in X-ray Coronary Angiographycited by 5

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 0; 2024: 0; 2025: 1; 2026: 2.

4 papers.

  1. 2022R2C-GAN: Restore-to-Classify Generative Adversarial Networks for Blind X-Ray Restoration and COVID-19 Classificationcited by 2
  2. 2025Advancing Embodied Intelligence in Robotic-Assisted Endovascular Procedures: A Systematic Review of AI Solutionscited by 2
  3. 2026MorVess: Morphology-Aware Pulmonary Vessel Segmentation Networkcited by 0
  4. 2026HTC-SGA Former: A Hybrid Transformer-CNN Network with Self-Guided Attention and a New Boundary-Weighted Adaptive Loss for Coronary DSA Vessel Segmentationcited by 0