Automatic diagnosis of cardiac magnetic resonance images based on semi-supervised learning

• URL: http://arxiv.org/abs/2405.14300v1
• Date: Thu, 23 May 2024 08:21:03 GMT
• Title: Automatic diagnosis of cardiac magnetic resonance images based on semi-supervised learning
• Authors: Hejun Huang, Zuguo Chen, Yi Huang, Guangqiang Luo, Chaoyang Chen, Youzhi Song,
• Abstract summary: This paper introduces a semi-supervised model for automatic segmentation of cardiac images and auxiliary diagnosis. The model achieves fully automated, high-precision segmentation of cardiac images, extraction of features, calculation of clinical indices, and prediction of diseases.
• Score: 4.568207745795955
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Cardiac magnetic resonance imaging (MRI) is a pivotal tool for assessing cardiac function. Precise segmentation of cardiac structures is imperative for accurate cardiac functional evaluation. This paper introduces a semi-supervised model for automatic segmentation of cardiac images and auxiliary diagnosis. By harnessing cardiac MRI images and necessitating only a small portion of annotated image data, the model achieves fully automated, high-precision segmentation of cardiac images, extraction of features, calculation of clinical indices, and prediction of diseases. The provided segmentation results, clinical indices, and prediction outcomes can aid physicians in diagnosis, thereby serving as auxiliary diagnostic tools. Experimental results showcase that this semi-supervised model for automatic segmentation of cardiac images and auxiliary diagnosis attains high accuracy in segmentation and correctness in prediction, demonstrating substantial practical guidance and application value.

Related papers

• Semantic Segmentation for Preoperative Planning in Transcatheter Aortic Valve Replacement [61.573750959726475]
  We consider medical guidelines for preoperative planning of the transcatheter aortic valve replacement (TAVR) and identify tasks that may be supported via semantic segmentation models.<n>We first derive fine-grained TAVR-relevant pseudo-labels from coarse-grained anatomical information, in order to train segmentation models and quantify how well they are able to find these structures in the scans.
  arXiv  Detail & Related papers  (2025-07-22T13:24:45Z)
• Robust Deep Learning for Myocardial Scar Segmentation in Cardiac MRI with Noisy Labels [0.493599216374976]
  We propose a robust deep-learning pipeline for automated myocardial scar detection and segmentation by fine-tuning state-of-the-art models.<n>We evaluate the model's performance on both acute and chronic cases and demonstrate its ability to produce accurate and smooth segmentations.
  arXiv  Detail & Related papers  (2025-06-26T11:21:58Z)
• Global and Local Contrastive Learning for Joint Representations from Cardiac MRI and ECG [40.407824759778784]
  PTACL (Patient and Temporal Alignment Contrastive Learning) is a multimodal contrastive learning framework that enhances ECG representations by integrating-temporal information from CMR.<n>We evaluate PTACL on paired ECG-CMR data from 27,951 subjects in the UK Biobank.<n>Our results highlight the potential of PTACL to enhance non-invasive cardiac diagnostics using ECG.
  arXiv  Detail & Related papers  (2025-06-24T17:19:39Z)
• IntelliCardiac: An Intelligent Platform for Cardiac Image Segmentation and Classification [8.793377489331466]
  We introduce IntelliCardiac, a web-based platform for the automatic segmentation of 4D cardiac images and disease classification.<n>IntelliCardiac uses an AI model trained on the publicly accessible ACDC dataset to identify essential heart structures and categorize cardiac diseases.<n>The system supports analysis of both the right and left ventricles as well as myocardium, and then classifies patient's cardiac images into five diagnostic categories.
  arXiv  Detail & Related papers  (2025-05-05T04:09:31Z)
• Cardiac MRI Semantic Segmentation for Ventricles and Myocardium using Deep Learning [0.0]
  Automated noninvasive cardiac diagnosis plays a critical role in the early detection of cardiac disorders. We present a model to improve semantic segmentation of cardiac images.
  arXiv  Detail & Related papers  (2025-04-18T00:54:30Z)
• Multi-Stage Segmentation and Cascade Classification Methods for Improving Cardiac MRI Analysis [15.236546465767026]
  We introduce a novel deep learning-based approach to segmentation and classification of cardiac magnetic resonance images. The method improved segmentation accuracy, achieving a Dice coefficient of 0.974 for the left ventricle and 0.947 for the right ventricle. For classification, a cascade of deep learning classifiers was employed to distinguish heart conditions, including hypertrophic cardiomyopathy, myocardial infarction, and dilated cardiomyopathy.
  arXiv  Detail & Related papers  (2024-12-12T15:53:14Z)
• Classification, Regression and Segmentation directly from k-Space in Cardiac MRI [11.690226907936903]
  We propose KMAE, a Transformer-based model specifically designed to process k-space data directly. KMAE can handle critical cardiac disease classification, relevant phenotype regression, and cardiac segmentation tasks. We utilize this model to investigate the potential of k-space-based diagnosis in cardiac MRI.
  arXiv  Detail & Related papers  (2024-07-29T15:35:35Z)
• How good nnU-Net for Segmenting Cardiac MRI: A Comprehensive Evaluation [2.5725730509014353]
  In this study, we evaluate the performance of nnU-Net in segmenting cardiac magnetic resonance images (MRIs) We employ various nnU-Net configurations, including 2D, 3D full resolution, 3D low resolution, 3D cascade, and ensemble models.
  arXiv  Detail & Related papers  (2024-07-26T01:47:20Z)
• CMRxRecon2024: A Multi-Modality, Multi-View K-Space Dataset Boosting Universal Machine Learning for Accelerated Cardiac MRI [39.0162369912624]
  The CMRxRecon2024 dataset is the largest and most diverse publicly available cardiac k-space dataset. It is acquired from 330 healthy volunteers, covering commonly used modalities, anatomical views, and acquisition trajectories in clinical cardiac MRI.
  arXiv  Detail & Related papers  (2024-06-27T09:50:20Z)
• A Comparative Analysis of U-Net-based models for Segmentation of Cardiac MRI [0.0]
  This paper aims to explore the application of deep learning techniques in the semantic segmentation of Cardiac short-axis MRI (Magnetic Resonance Imaging) images. The focus centers on implementing various architectures that are derivatives of U-Net, to effectively isolate specific parts of the heart for comprehensive anatomical and functional analysis.
  arXiv  Detail & Related papers  (2024-01-18T13:51:20Z)
• Ensemble Learning of Myocardial Displacements for Myocardial Infarction Detection in Echocardiography [15.153823114115307]
  Early detection and localization of myocardial infarction can reduce the severity of cardiac damage. Deep learning techniques have shown promise for detecting MI in echocardiographic images. Our study introduces a robust method that combines features from multiple segmentation models to improve MI classification performance.
  arXiv  Detail & Related papers  (2023-03-12T20:16:14Z)
• Preservation of High Frequency Content for Deep Learning-Based Medical Image Classification [74.84221280249876]
  An efficient analysis of large amounts of chest radiographs can aid physicians and radiologists. We propose a novel Discrete Wavelet Transform (DWT)-based method for the efficient identification and encoding of visual information.
  arXiv  Detail & Related papers  (2022-05-08T15:29:54Z)
• Improving Classification Model Performance on Chest X-Rays through Lung Segmentation [63.45024974079371]
  We propose a deep learning approach to enhance abnormal chest x-ray (CXR) identification performance through segmentations. Our approach is designed in a cascaded manner and incorporates two modules: a deep neural network with criss-cross attention modules (XLSor) for localizing lung region in CXR images and a CXR classification model with a backbone of a self-supervised momentum contrast (MoCo) model pre-trained on large-scale CXR data sets.
  arXiv  Detail & Related papers  (2022-02-22T15:24:06Z)
• MyoPS: A Benchmark of Myocardial Pathology Segmentation Combining Three-Sequence Cardiac Magnetic Resonance Images [84.02849948202116]
  This work defines a new task of medical image analysis, i.e., to perform myocardial pathology segmentation (MyoPS) MyoPS combines three-sequence cardiac magnetic resonance (CMR) images, which was first proposed in the MyoPS challenge, in conjunction with MICCAI 2020. The challenge provided 45 paired and pre-aligned CMR images, allowing algorithms to combine the complementary information from the three CMR sequences for pathology segmentation.
  arXiv  Detail & Related papers  (2022-01-10T06:37:23Z)
• Machine Learning-based Efficient Ventricular Tachycardia Detection Model of ECG Signal [0.0]
  In primary diagnosis and analysis of heart defects, an ECG signal plays a significant role. This paper presents a model for the prediction of ventricular tachycardia arrhythmia using noise filtering, a unique set of ECG features, and a machine learning-based classifier model.
  arXiv  Detail & Related papers  (2021-12-24T05:56:09Z)
• 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)
• A Global Benchmark of Algorithms for Segmenting Late Gadolinium-Enhanced Cardiac Magnetic Resonance Imaging [90.29017019187282]
  " 2018 Left Atrium Challenge" using 154 3D LGE-MRIs, currently the world's largest cardiac LGE-MRI dataset. Analyse of the submitted algorithms using technical and biological metrics was performed. Results show the top method achieved a dice score of 93.2% and a mean surface to a surface distance of 0.7 mm.
  arXiv  Detail & Related papers  (2020-04-26T08:49:17Z)
• Heart Sound Segmentation using Bidirectional LSTMs with Attention [37.62160903348547]
  We propose a novel framework for the segmentation of phonocardiogram (PCG) signals into heart states. We exploit recent advancements in attention based learning to segment the PCG signal. The proposed method attains state-of-the-art performance on multiple benchmarks including both human and animal heart recordings.
  arXiv  Detail & Related papers  (2020-04-02T02:09:11Z)

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.