CPR-GCN: Conditional Partial-Residual Graph Convolutional Network in Automated Anatomical Labeling of Coronary Arteries

• URL: http://arxiv.org/abs/2003.08560v4
• Date: Sat, 18 Apr 2020 03:07:03 GMT
• Title: CPR-GCN: Conditional Partial-Residual Graph Convolutional Network in Automated Anatomical Labeling of Coronary Arteries
• Abstract summary: We propose a conditional partial-residual graph convolutional network (CPR-GCN), which takes both position and CT image into consideration. According to the five-fold cross-validation, our CPR-GCN yields 95.8% meanRecall, 95.4% meanPrecision and 0.955 meanF1, which outperforms state-of-the-art approaches.
• Score: 34.47036303404335
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Automated anatomical labeling plays a vital role in coronary artery disease diagnosing procedure. The main challenge in this problem is the large individual variability inherited in human anatomy. Existing methods usually rely on the position information and the prior knowledge of the topology of the coronary artery tree, which may lead to unsatisfactory performance when the main branches are confusing. Motivated by the wide application of the graph neural network in structured data, in this paper, we propose a conditional partial-residual graph convolutional network (CPR-GCN), which takes both position and CT image into consideration, since CT image contains abundant information such as branch size and spanning direction. Two majority parts, a Partial-Residual GCN and a conditions extractor, are included in CPR-GCN. The conditions extractor is a hybrid model containing the 3D CNN and the LSTM, which can extract 3D spatial image features along the branches. On the technical side, the Partial-Residual GCN takes the position features of the branches, with the 3D spatial image features as conditions, to predict the label for each branches. While on the mathematical side, our approach twists the partial differential equation (PDE) into the graph modeling. A dataset with 511 subjects is collected from the clinic and annotated by two experts with a two-phase annotation process. According to the five-fold cross-validation, our CPR-GCN yields 95.8% meanRecall, 95.4% meanPrecision and 0.955 meanF1, which outperforms state-of-the-art approaches.

Related papers

• NISF++: Geometrically-grounded implicit representations of 3D+time cardiac function from 2D short- and long-axis MR views [17.503826895619614]
  This paper builds upon previous work on neural implicit segmentation functions (NISF) to overcome unaddressed challenges in cardiac function modeling.<n>Our architecture builds a shared 3D+time representations from all available acquisition planes regardless of orientation.<n>Our in-plane segmentation performance is on-par with existing CMR segmentation methods and explore how the majority of failure cases arise from limitations in the ground-truth segmentation.
  arXiv  Detail & Related papers  (2026-08-01T16:35:56Z)
• Contrastive Graph Modeling for Cross-Domain Few-Shot Medical Image Segmentation [58.41482540044918]
  Cross-domain few-shot medical image segmentation (CD-FSMIS) offers a promising and data-efficient solution for medical applications.<n>We present Contrastive Graph Modeling (C-Graph), a framework that leverages the structural consistency of medical images as a reliable domain-transferable prior.
  arXiv  Detail & Related papers  (2025-12-25T14:00:17Z)
• CT-GRAPH: Hierarchical Graph Attention Network for Anatomy-Guided CT Report Generation [4.376648893167674]
  We propose CT-GRAPH, a hierarchical graph attention network that explicitly models radiological knowledge.<n>Our method leverages pretrained 3D medical feature encoders to obtain global and organ-level features.<n>We show that our method achieves a substantial improvement of absolute 7.9% in F1 score over current state-of-the-art methods.
  arXiv  Detail & Related papers  (2025-08-07T13:18:03Z)
• Neovascularization Segmentation via a Multilateral Interaction-Enhanced Graph Convolutional Network [48.788798029027085]
  This paper proposes a novel multilateral graph convolutional interaction-enhanced CNV segmentation network (MTG-Net)<n> MTG-Net consists of a multi-task framework and two graph-based cross-task modules: Multilateral Interaction Graph Reasoning (MIGR) and Multilateral Reinforcement Graph Reasoning (MRGR)<n> Experimental results demonstrate that MTG-Net outperforms existing methods, achieving a Dice socre of 87.21% for region segmentation and 88.12% for vessel segmentation.
  arXiv  Detail & Related papers  (2025-08-05T08:10:19Z)
• Myocardial Region-guided Feature Aggregation Net for Automatic Coronary artery Segmentation and Stenosis Assessment using Coronary Computed Tomography Angiography [13.885760158090692]
  Myocardial Region-guided Feature Aggregation Net is a novel U-shaped dual-encoder architecture that integrates anatomical prior knowledge to enhance robustness in coronary artery segmentation. Our framework incorporates three key innovations: (1) a Myocardial Region-guided Module that directs attention to coronary regions via bridging expansion and multi-scale feature fusion, (2) a Residual Feature Extraction Module that combines parallel spatial channel attention with residual blocks to enhance local-global feature discrimination, and (3) a Multi-scale Feature Fusion Module for adaptive aggregation of hierarchical vascular features.
  arXiv  Detail & Related papers  (2025-04-27T16:43:52Z)
• A Graph-Based Framework for Interpretable Whole Slide Image Analysis [86.37618055724441]
  We develop a framework that transforms whole-slide images into biologically-informed graph representations.<n>Our approach builds graph nodes from tissue regions that respect natural structures, not arbitrary grids.<n>We demonstrate strong performance on challenging cancer staging and survival prediction tasks.
  arXiv  Detail & Related papers  (2025-03-14T20:15:04Z)
• Accurate Fine-Grained Segmentation of Human Anatomy in Radiographs via Volumetric Pseudo-Labeling [66.75096111651062]
  We created a large-scale dataset of 10,021 thoracic CTs with 157 labels. We applied an ensemble of 3D anatomy segmentation models to extract anatomical pseudo-labels. Our resulting segmentation models demonstrated remarkable performance on CXR.
  arXiv  Detail & Related papers  (2023-06-06T18:01:08Z)
• Segmentation of Aortic Vessel Tree in CT Scans with Deep Fully Convolutional Networks [4.062948258086793]
  Automatic and accurate segmentation of aortic vessel tree (AVT) in computed tomography (CT) scans is crucial for early detection, diagnosis and prognosis of aortic diseases. We use two-stage fully convolutional networks (FCNs) to automatically segment AVT in scans from multiple centers.
  arXiv  Detail & Related papers  (2023-05-16T22:24:01Z)
• Semantic segmentation of surgical hyperspectral images under geometric domain shifts [69.91792194237212]
  We present the first analysis of state-of-the-art semantic segmentation networks in the presence of geometric out-of-distribution (OOD) data. We also address generalizability with a dedicated augmentation technique termed "Organ Transplantation" Our scheme improves on the SOA DSC by up to 67 % (RGB) and 90 % (HSI) and renders performance on par with in-distribution performance on real OOD test data.
  arXiv  Detail & Related papers  (2023-03-20T09:50:07Z)
• Two-Stream Graph Convolutional Network for Intra-oral Scanner Image Segmentation [133.02190910009384]
  We propose a two-stream graph convolutional network (i.e., TSGCN) to handle inter-view confusion between different raw attributes. Our TSGCN significantly outperforms state-of-the-art methods in 3D tooth (surface) segmentation.
  arXiv  Detail & Related papers  (2022-04-19T10:41:09Z)
• Multi-organ Segmentation Network with Adversarial Performance Validator [10.775440368500416]
  This paper introduces an adversarial performance validation network into a 2D-to-3D segmentation framework. The proposed network converts the 2D-coarse result to 3D high-quality segmentation masks in a coarse-to-fine manner, allowing joint optimization to improve segmentation accuracy. Experiments on the NIH pancreas segmentation dataset demonstrate the proposed network achieves state-of-the-art accuracy on small organ segmentation and outperforms the previous best.
  arXiv  Detail & Related papers  (2022-04-16T18:00:29Z)
• Improving anatomical plausibility in medical image segmentation via hybrid graph neural networks: applications to chest x-ray analysis [3.3382651833270587]
  We introduce HybridGNet, an encoder-decoder neural architecture that leverages standard convolutions for image feature encoding and graph convolutional neural networks (GCNNs) to decode plausible representations of anatomical structures. A novel image-to-graph skip connection layer allows localized features to flow from standard convolutional blocks to GCNN blocks, and show that it improves segmentation accuracy.
  arXiv  Detail & Related papers  (2022-03-21T13:37:23Z)
• Symmetry-Enhanced Attention Network for Acute Ischemic Infarct Segmentation with Non-Contrast CT Images [50.55978219682419]
  We propose a symmetry enhanced attention network (SEAN) for acute ischemic infarct segmentation. Our proposed network automatically transforms an input CT image into the standard space where the brain tissue is bilaterally symmetric. The proposed SEAN outperforms some symmetry-based state-of-the-art methods in terms of both dice coefficient and infarct localization.
  arXiv  Detail & Related papers  (2021-10-11T07:13:26Z)
• PSGR: Pixel-wise Sparse Graph Reasoning for COVID-19 Pneumonia Segmentation in CT Images [83.26057031236965]
  We propose a pixel-wise sparse graph reasoning (PSGR) module to enhance the modeling of long-range dependencies for COVID-19 infected region segmentation in CT images. The PSGR module avoids imprecise pixel-to-node projections and preserves the inherent information of each pixel for global reasoning. The solution has been evaluated against four widely-used segmentation models on three public datasets.
  arXiv  Detail & Related papers  (2021-08-09T04:58:23Z)
• An Uncertainty-Driven GCN Refinement Strategy for Organ Segmentation [53.425900196763756]
  We propose a segmentation refinement method based on uncertainty analysis and graph convolutional networks. We employ the uncertainty levels of the convolutional network in a particular input volume to formulate a semi-supervised graph learning problem. We show that our method outperforms the state-of-the-art CRF refinement method by improving the dice score by 1% for the pancreas and 2% for spleen.
  arXiv  Detail & Related papers  (2020-12-06T18:55:07Z)
• Gastric histopathology image segmentation using a hierarchical conditional random field [16.920864110707747]
  A novel Conditional Random Field (HCRF) based Gastric Histopathology Image (GHIS) method is proposed. Our HCRF model demonstrates high segmentation performance and shows its effectiveness and future potential in the GHIS field.
  arXiv  Detail & Related papers  (2020-03-03T02:44:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.