Fully Automatic Scar Segmentation for Late Gadolinium Enhancement MRI Images in Left Ventricle with Myocardial Infarction

Segmentation and severity classification of scar tissues in LGE-CMR images using HDResC-Net with Flamingo gannet search optimization

Late gadolinium enhanced-cardiac magnetic resonance (LGE-CMR) images play a critical role in evaluating cardiac pathology, where scar tissue serves as a vital indicator impacting prognosis and treatment decisions. However, accurately segmenting scar tissues and assessing their severity present challenges due to complex tissue composition and imaging artifacts. Existing methods often lack precision and robustness, limiting their clinical applicability. This work proposes a novel methodology that integrates the optimal segmentation algorithm (OSA) for segmentation and Flamingo Gannet search optimization-enabled hybrid deep residual convolutional network (FGSO-HDResC-Net) for severity classification of scar tissues in LGE-CMR images. Initially, the input image is pre-processed by using the adaptive Gabor Kuwahara filter. Then, the approach combines myocardium segmentation via region-based convolutional neural network and scar segmentation using OSA. Subsequently, FGSO-HDResC-Net integrates feature extraction and classification while optimizing hyperparameters through Flamingo Gannet search optimization. The feature extraction stage introduces two sets of techniques: localization features with texture analysis and spatial/temporal features using a deep residual network, complemented by feature fusion using the fractional concept. These features are inputted into a customized 1D convolutional neural network model for severity classification. Through comprehensive evaluation, the effectiveness of FGSO-HDResC-Net in accurately classifying scar tissue severity is demonstrated, offering improved disease assessment and treatment planning for cardiac patients. Moreover, the proposed FGSO-HDResC-Net model demonstrated superior performance, achieving an accuracy of 96.45%, a true positive rate of 95.42%, a true negative rate of 96.48%, a positive predictive value of 94.20%, and a negative predictive value of 94.18%. The accuracy of the devised model is 14.50%, 12.99%, 10.74%, 9.75%, 12.79%, and 11.26% improved than the traditional models.

Deep learning for cardiac imaging: focus on myocardial diseases, a narrative review

The integration of computational technologies into cardiology has significantly advanced the diagnosis and management of cardiovascular diseases. Computational cardiology, particularly, through cardiovascular imaging and informatics, enables a precise diagnosis of myocardial diseases utilizing techniques such as echocardiography, cardiac magnetic resonance imaging, and computed tomography. Early-stage disease classification, especially in asymptomatic patients, benefits from these advancements, potentially altering disease progression and improving patient outcomes. Automatic segmentation of myocardial tissue using deep learning (DL) algorithms improves efficiency and consistency in analyzing large patient populations. Radiomic analysis can reveal subtle disease characteristics from medical images and can enhance disease detection, enable patient stratification, and facilitate monitoring of disease progression and treatment response. Radiomic biomarkers have already demonstrated high diagnostic accuracy in distinguishing myocardial pathologies and promise treatment individualization in cardiology, earlier disease detection, and disease monitoring. In this context, this narrative review explores the current state of the art in DL applications in medical imaging (CT, CMR, echocardiography, and SPECT), focusing on automatic segmentation, radiomic feature phenotyping, and prediction of myocardial diseases, while also discussing challenges in integration of DL models in clinical practice.

Preliminary Study: Learning the Impact of Simulation Time on Reentry Location and Morphology Induced by Personalized Cardiac Modeling

Personalized cardiac modeling is widely used for studying the mechanisms of cardiac arrythmias. Due to the high demanding of computational resource of modeling, the arrhythmias induced in the models are usually simulated for just a few seconds. In clinic, it is common that arrhythmias last for more than several minutes and the morphologies of reentries are not always stable, so it is not clear that whether the simulation of arrythmias for just a few seconds is long enough to match the arrhythmias detected in patients. This study aimed to observe how long simulation of the induced arrhythmias in the personalized cardiac models is sufficient to match the arrhythmias detected in patients. A total of 5 contrast enhanced MRI datasets of patient hearts with myocardial infarction were used in this study. Then, a classification method based on Gaussian mixture model was used to detect the infarct tissue. For each reentry, 3 s and 10 s were simulated. The characteristics of each reentry simulated for different duration were studied. Reentries were induced in all 5 ventricular models and sustained reentries were induced at 39 stimulation sites in the model. By analyzing the simulation results, we found that 41% of the sustained reentries in the 3 s simulation group terminated in the longer simulation groups (10 s). The second finding in our simulation was that only 23.1% of the sustained reentries in the 3 s simulation did not change location and morphology in the extended 10 s simulation. The third finding was that 35.9% reentries were stable in the 3 s simulation and should be extended for the simulation time. The fourth finding was that the simulation results in 10 s simulation matched better with the clinical measurements than the 3 s simulation. It was shown that 10 s simulation was sufficient to make simulation results stable. The findings of this study not only improve the simulation accuracy, but also reduce the unnecessary simulation time to achieve the optimal use of computer resources to improve the simulation efficiency and shorten the simulation time to meet the time node requirements of clinical operation on patients.