O2CTA: Introducing Annotations from OCT to CCTA in Coronary Plaque Analysis

• URL: http://arxiv.org/abs/2303.06358v2
• Date: Fri, 11 Aug 2023 05:36:12 GMT
• Title: O2CTA: Introducing Annotations from OCT to CCTA in Coronary Plaque Analysis
• Authors: Jun Li, Kexin Li, Yafeng Zhou, S. Kevin Zhou
• Abstract summary: Coronary CT angiography (CCTA) is widely used for artery imaging and determining the stenosis degree. It can be settled by invasive optical coherence tomography ( OCT) without much trouble for physicians, but bringing higher costs and potential risks to patients. We propose a method to handle the O2CTA problem. CCTA scans are first reconstructed into multi-planar reformatted (MPR) images, which agree with OCT images in term of semantic contents. The artery segment in OCT, which is manually labelled, is then spatially aligned with the entire artery in MPR images via the proposed alignment strategy.
• Score: 19.099761377777412
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Targeted diagnosis and treatment plans for patients with coronary artery disease vary according to atherosclerotic plaque component. Coronary CT angiography (CCTA) is widely used for artery imaging and determining the stenosis degree. However, the limited spatial resolution and susceptibility to artifacts fail CCTA in obtaining lumen morphological characteristics and plaque composition. It can be settled by invasive optical coherence tomography (OCT) without much trouble for physicians, but bringing higher costs and potential risks to patients. Therefore, it is clinically critical to introduce annotations of plaque tissue and lumen characteristics from OCT to paired CCTA scans, denoted as \textbf{the O2CTA problem} in this paper. We propose a method to handle the O2CTA problem. CCTA scans are first reconstructed into multi-planar reformatted (MPR) images, which agree with OCT images in term of semantic contents. The artery segment in OCT, which is manually labelled, is then spatially aligned with the entire artery in MPR images via the proposed alignment strategy. Finally, a classification model involving a 3D CNN and a Transformer, is learned to extract local features and capture dependence along arteries. Experiments on 55 paired OCT and CCTA we curate demonstrate that it is feasible to classify the CCTA based on the OCT labels, with an accuracy of 86.2%, while the manual readings of OCT and CCTA vary significantly, with a Kappa coefficient of 0.113. We will make our source codes, models, data, and results publicly available to benefit the research community.

Related papers

• AGFA-Net: Attention-Guided and Feature-Aggregated Network for Coronary Artery Segmentation using Computed Tomography Angiography [5.583495103569884]
  We propose an attention-guided, feature-aggregated 3D deep network (AGFA-Net) for coronary artery segmentation using CCTA images. AGFA-Net leverages attention mechanisms and feature refinement modules to capture salient features and enhance segmentation accuracy. Evaluation on a dataset comprising 1,000 CCTA scans demonstrates AGFA-Net's superior performance, achieving an average Dice coefficient similarity of 86.74% and a Hausdorff distance of 0.23 mm.
  arXiv  Detail & Related papers  (2024-06-13T01:04:47Z)
• Coronary artery segmentation in non-contrast calcium scoring CT images using deep learning [2.2687766762329886]
  We introduce a deep learning algorithm for segmenting coronary arteries in non-contrast cardiac CT images. We propose a novel method for manual mesh-to-image registration, which is used to create our test-GT. The experimental study shows that the trained model has significantly higher accuracy than the GT used for training, and leads to the Dice and clDice metrics close to the interrater variability.
  arXiv  Detail & Related papers  (2024-03-04T23:40:02Z)
• Pericoronary adipose tissue feature analysis in CT calcium score images with comparison to coronary CTA [3.9387305699226087]
  Pericoronary adipose tissue (PCAT) is associated with major adverse cardiovascular events (MACE) PCAT features from coronary CT calcium score (CCTA) have been shown to be associated with cardiovascular risk but are potentially confounded by iodine. We developed a novel axial-disk method giving regions for analyzing PCAT features in three main coronary arteries.
  arXiv  Detail & Related papers  (2024-01-28T03:25:38Z)
• 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)
• Deep learning network to correct axial and coronal eye motion in 3D OCT retinal imaging [65.47834983591957]
  We propose deep learning based neural networks to correct axial and coronal motion artifacts in OCT based on a single scan. The experimental result shows that the proposed method can effectively correct motion artifacts and achieve smaller error than other methods.
  arXiv  Detail & Related papers  (2023-05-27T03:55:19Z)
• Morphology-based non-rigid registration of coronary computed tomography and intravascular images through virtual catheter path optimization [0.5525871666098097]
  We present a semi-automatic segmentation-based framework for both rigid and non-rigid matching of intravascular images to CCTA images. Our results indicate that our non-rigid registration significantly outperforms other co-registration approaches for luminal bifurcation alignment.
  arXiv  Detail & Related papers  (2022-12-30T21:48:32Z)
• Computed tomography coronary angiogram images, annotations and associated data of normal and diseased arteries [8.516530964229814]
  Computed Tomography Coronary Angiography (CTCA) is a non-invasive method to evaluate coronary artery anatomy and disease. To our knowledge there is no public dataset that includes centrelines and segmentation of the full coronary tree. Data can be used for a variety of research purposes, such as 3D printing patient-specific models, development and validation of segmentation algorithms, education and training of medical personnel and in-silico analyses such as testing of medical devices.
  arXiv  Detail & Related papers  (2022-11-03T14:50:43Z)
• CNN Filter Learning from Drawn Markers for the Detection of Suggestive Signs of COVID-19 in CT Images [58.720142291102135]
  We propose a method that does not require either large annotated datasets or backpropagation to estimate the filters of a convolutional neural network (CNN) For a few CT images, the user draws markers at representative normal and abnormal regions. The method generates a feature extractor composed of a sequence of convolutional layers, whose kernels are specialized in enhancing regions similar to the marked ones.
  arXiv  Detail & Related papers  (2021-11-16T15:03:42Z)
• 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)
• AI-based Aortic Vessel Tree Segmentation for Cardiovascular Diseases Treatment: Status Quo [55.04215695343928]
  The aortic vessel tree is composed of the aorta and its branching arteries. We systematically review computing techniques for the automatic and semi-automatic segmentation of the aortic vessel tree.
  arXiv  Detail & Related papers  (2021-08-06T08:18:28Z)
• Synergistic Learning of Lung Lobe Segmentation and Hierarchical Multi-Instance Classification for Automated Severity Assessment of COVID-19 in CT Images [61.862364277007934]
  We propose a synergistic learning framework for automated severity assessment of COVID-19 in 3D CT images. A multi-task deep network (called M$2$UNet) is then developed to assess the severity of COVID-19 patients. Our M$2$UNet consists of a patch-level encoder, a segmentation sub-network for lung lobe segmentation, and a classification sub-network for severity assessment.
  arXiv  Detail & Related papers  (2020-05-08T03:16:15Z)

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.