On the Automated Segmentation of Epicardial and Mediastinal Cardiac Adipose Tissues Using Classification Algorithms

• URL: http://arxiv.org/abs/2208.14352v1
• Date: Tue, 30 Aug 2022 15:57:02 GMT
• Title: On the Automated Segmentation of Epicardial and Mediastinal Cardiac Adipose Tissues Using Classification Algorithms
• Authors: \'Erick Oliveira Rodrigues and Felipe Fernandes Cordeiro de Morais and Aura Conci
• Abstract summary: This work proposes a novel technique for the automatic segmentation of cardiac fat pads. The technique is based on applying classification algorithms to the segmentation of cardiac CT images. Experimental results have shown that the mean accuracy for the classification of epicardial and mediastinal fats has been 98.4%.
• Score: 0.9176056742068814
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The quantification of fat depots on the surroundings of the heart is an accurate procedure for evaluating health risk factors correlated with several diseases. However, this type of evaluation is not widely employed in clinical practice due to the required human workload. This work proposes a novel technique for the automatic segmentation of cardiac fat pads. The technique is based on applying classification algorithms to the segmentation of cardiac CT images. Furthermore, we extensively evaluate the performance of several algorithms on this task and discuss which provided better predictive models. Experimental results have shown that the mean accuracy for the classification of epicardial and mediastinal fats has been 98.4% with a mean true positive rate of 96.2%. On average, the Dice similarity index, regarding the segmented patients and the ground truth, was equal to 96.8%. Therfore, our technique has achieved the most accurate results for the automatic segmentation of cardiac fats, to date.

Related papers

• Self-supervised Anomaly Detection Pretraining Enhances Long-tail ECG Diagnosis [32.37717219026923]
  Current computer-aided ECG diagnostic systems struggle with the underdetection of rare but critical cardiac anomalies. This study introduces a novel approach using self-supervised anomaly detection pretraining to address this limitation. The anomaly detection model is specifically designed to detect and localize subtle deviations from normal cardiac patterns.
  arXiv  Detail & Related papers  (2024-08-30T09:48:47Z)
• Anomaly Detection in Electrocardiograms: Advancing Clinical Diagnosis Through Self-Supervised Learning [32.37717219026923]
  Existing systems often miss rare cardiac anomalies that could be precursors to serious, life-threatening issues or alterations in the cardiac macro/microstructure. We focus on self-supervised anomaly detection (AD), training exclusively on normal ECGs to recognize deviations indicating anomalies. We introduce a novel self-supervised learning framework for ECG AD, utilizing a vast dataset of normal ECGs to autonomously detect and localize cardiac anomalies.
  arXiv  Detail & Related papers  (2024-04-07T12:15:53Z)
• Development of Automated Neural Network Prediction for Echocardiographic Left ventricular Ejection Fraction [36.58987036154144]
  This paper proposes a new pipeline method based on deep neural networks and ensemble learning to quantify left ventricular ejection fraction (LVEF) The method was developed and validated in an open-source dataset containing 10,030 echocardiograms. This study demonstrates that an automated neural network-based calculation of LVEF is comparable to expert clinicians performing frame-by-frame manual evaluation of cardiac systolic function.
  arXiv  Detail & Related papers  (2024-03-18T18:09:22Z)
• Attention-based Saliency Maps Improve Interpretability of Pneumothorax Classification [52.77024349608834]
  To investigate chest radiograph (CXR) classification performance of vision transformers (ViT) and interpretability of attention-based saliency. ViTs were fine-tuned for lung disease classification using four public data sets: CheXpert, Chest X-Ray 14, MIMIC CXR, and VinBigData. ViTs had comparable CXR classification AUCs compared with state-of-the-art CNNs.
  arXiv  Detail & Related papers  (2023-03-03T12:05:41Z)
• ECG-Based Patient Identification: A Comprehensive Evaluation Across Health and Activity Conditions [0.0]
  This paper presents a novel approach for patient identification in healthcare systems using electrocardiogram signals. A convolutional neural network (CNN) is employed to classify users based on electrocardiomatrices, a specific type of image derived from ECG signals. The proposed identification system is evaluated in multiple databases, achieving up to 99.84% accuracy on healthy subjects, 97.09% on patients with cardiovascular diseases, and 97.89% on mixed populations including both healthy and arrhythmic patients.
  arXiv  Detail & Related papers  (2023-02-13T17:14:55Z)
• Accelerometry-based classification of circulatory states during out-of-hospital cardiac arrest [1.2109519547057512]
  We developed a machine learning algorithm to automatically predict the circulatory state during cardiac arrest treatment. The algorithm was trained based on 917 cases from the German Resuscitation Registry.
  arXiv  Detail & Related papers  (2022-05-13T10:03:56Z)
• A novel approach for the automated segmentation and volume quantification of cardiac fats on computed tomography [0.9786690381850356]
  We propose a unified method for an autonomous segmentation and quantification of two types of cardiac fats. The segmented fats are termed epicardial and mediastinal, and stand apart from each other by the pericardium. The proposed methodology mainly comprises registration and classification algorithms to perform the desired segmentation.
  arXiv  Detail & Related papers  (2021-12-21T17:38:06Z)
• Segmentation-free Heart Pathology Detection Using Deep Learning [12.065014651638943]
  We propose a novel segmentation-free heart sound classification method. Specifically, we apply discrete wavelet transform to denoise the signal, followed by feature extraction and feature reduction. Support Vector Machines and Deep Neural Networks are utilised for classification.
  arXiv  Detail & Related papers  (2021-08-09T16:09:30Z)
• Ensemble machine learning approach for screening of coronary heart disease based on echocardiography and risk factors [19.076443235356873]
  We develop a machine learning approach that integrates a number of popular classification methods together by model stacking. We improve the CHD classification accuracy from around 70% to 87.7% on the testing set.
  arXiv  Detail & Related papers  (2021-05-20T11:04:58Z)
• Identification of Ischemic Heart Disease by using machine learning technique based on parameters measuring Heart Rate Variability [50.591267188664666]
  In this study, 18 non-invasive features (age, gender, left ventricular ejection fraction and 15 obtained from HRV) of 243 subjects were used to train and validate a series of several ANN. The best result was obtained using 7 input parameters and 7 hidden nodes with an accuracy of 98.9% and 82% for the training and validation dataset.
  arXiv  Detail & Related papers  (2020-10-29T19:14:41Z)
• 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.