Automated recognition of the pericardium contour on processed CT images using genetic algorithms

• URL: http://arxiv.org/abs/2208.14375v1
• Date: Tue, 30 Aug 2022 16:35:41 GMT
• Title: Automated recognition of the pericardium contour on processed CT images using genetic algorithms
• Authors: E. O. Rodrigues and L. O. Rodrigues and L. S. N. Oliveira and A. Conci and P. Liatsis
• Abstract summary: We propose the use of Genetic Algorithms (GA) in tracing and recognizing the pericardium contour of the human heart. An optimal ellipse would be one that closely follows the pericardium contour and separates appropriately the epicardial and mediastinal fats of the human heart.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: This work proposes the use of Genetic Algorithms (GA) in tracing and recognizing the pericardium contour of the human heart using Computed Tomography (CT) images. We assume that each slice of the pericardium can be modelled by an ellipse, the parameters of which need to be optimally determined. An optimal ellipse would be one that closely follows the pericardium contour and, consequently, separates appropriately the epicardial and mediastinal fats of the human heart. Tracing and automatically identifying the pericardium contour aids in medical diagnosis. Usually, this process is done manually or not done at all due to the effort required. Besides, detecting the pericardium may improve previously proposed automated methodologies that separate the two types of fat associated to the human heart. Quantification of these fats provides important health risk marker information, as they are associated with the development of certain cardiovascular pathologies. Finally, we conclude that GA offers satisfiable solutions in a feasible amount of processing time.

Related papers

• Self-supervised inter-intra period-aware ECG representation learning for detecting atrial fibrillation [41.82319894067087]
  We propose an inter-intra period-aware ECG representation learning approach. Considering ECGs of atrial fibrillation patients exhibit the irregularity in RR intervals and the absence of P-waves, we develop specific pre-training tasks for interperiod and intraperiod representations. Our approach demonstrates remarkable AUC performances on the BTCH dataset, textiti.e., 0.953/0.996 for paroxysmal/persistent atrial fibrillation detection.
  arXiv  Detail & Related papers  (2024-10-08T10:03:52Z)
• 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)
• Digital twinning of cardiac electrophysiology models from the surface ECG: a geodesic backpropagation approach [39.36827689390718]
  We introduce a novel method, Geodesic-BP, to solve the inverse eikonal problem. We show that Geodesic-BP can reconstruct a simulated cardiac activation with high accuracy in a synthetic test case. Given the future shift towards personalized medicine, Geodesic-BP has the potential to help in future functionalizations of cardiac models.
  arXiv  Detail & Related papers  (2023-08-16T14:57:12Z)
• 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)
• Cardiac Adipose Tissue Segmentation via Image-Level Annotations [8.705311618392368]
  We develop a two-stage deep learning framework for cardiac adipose tissue segmentation using image-level annotations on OCT images of human cardiac substrates. Our study bridges the gap between the demand on automatic tissue analysis and the lack of high-quality pixel-wise annotations.
  arXiv  Detail & Related papers  (2022-06-09T02:55:35Z)
• An Algorithm for the Labeling and Interactive Visualization of the Cerebrovascular System of Ischemic Strokes [59.116811751334225]
  VirtualDSA++ is an algorithm designed to segment and label the cerebrovascular tree on CTA scans. We extend the labeling mechanism for the cerebral arteries to identify occluded vessels. We present the generic concept of iterative systematic search for pathways on all nodes of said model, which enables new interactive features.
  arXiv  Detail & Related papers  (2022-04-26T14:20:26Z)
• Echocardiography Segmentation with Enforced Temporal Consistency [10.652677452009627]
  We propose a framework to learn the 2D+time long-axis cardiac shape. The identification and correction of cardiac inconsistencies relies on a constrained autoencoder trained to learn a physiologically interpretable embedding of cardiac shapes.
  arXiv  Detail & Related papers  (2021-12-03T16:09:32Z)
• Automatic Identification of the End-Diastolic and End-Systolic Cardiac Frames from Invasive Coronary Angiography Videos [6.203906656404265]
  The identification of proper image frames at the end-diastolic (ED) and end-systolic (ES) frames during the review of invasive coronary angiograms is important. The current identification method primarily relies on visual interpretation, making it not only time-consuming but also less reproducible. We propose a new method to automatically identify angiographic image frames associated with the ED and ES cardiac phases by using the trajectories of key vessel points.
  arXiv  Detail & Related papers  (2021-10-06T15:16:55Z)
• 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)
• Cardiac Segmentation on CT Images through Shape-Aware Contour Attentions [1.212901554957637]
  The cardiac organ consists of multiple substructures, i.e., ventricles, atriums, aortas, arteries, veins, and myocardium. These cardiac substructures are proximate to each other and have indiscernible boundaries. We introduce a novel model to exploit shape and boundary-aware features.
  arXiv  Detail & Related papers  (2021-05-27T13:54:59Z)
• Segmentation of the Myocardium on Late-Gadolinium Enhanced MRI based on 2.5 D Residual Squeeze and Excitation Deep Learning Model [55.09533240649176]
  The aim of this work is to develop an accurate automatic segmentation method based on deep learning models for the myocardial borders on LGE-MRI. A total number of 320 exams (with a mean number of 6 slices per exam) were used for training and 28 exams used for testing. The performance analysis of the proposed ensemble model in the basal and middle slices was similar as compared to intra-observer study and slightly lower at apical slices.
  arXiv  Detail & Related papers  (2020-05-27T20:44:38Z)

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.