AIMC Topic: Myocardium

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Simulator-generated training datasets as an alternative to using patient data for machine learning: An example in myocardial segmentation with MRI.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Supervised Machine Learning techniques have shown significant potential in medical image analysis. However, the training data that need to be collected for these techniques in the field of MRI 1) may not be available, 2) may...

Identifying gross post-mortem organ images using a pre-trained convolutional neural network.

Journal of forensic sciences
Identifying organs/tissue and pathology on radiological and microscopic images can be performed using convolutional neural networks (CNN). However, there are scant studies on applying CNN to post-mortem gross images of visceral organs. This proof-of-...

Detecting myocardial scar using electrocardiogram data and deep neural networks.

Biological chemistry
Ischaemic heart disease is among the most frequent causes of death. Early detection of myocardial pathologies can increase the benefit of therapy and reduce the number of lethal cases. Presence of myocardial scar is an indicator for developing ischae...

Automated quantification of myocardial tissue characteristics from native T mapping using neural networks with uncertainty-based quality-control.

Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance
BACKGROUND: Tissue characterisation with cardiovascular magnetic resonance (CMR) parametric mapping has the potential to detect and quantify both focal and diffuse alterations in myocardial structure not assessable by late gadolinium enhancement. Nat...

Artificial Intelligence and Myocardial Contrast Enhancement Pattern.

Current cardiology reports
PURPOSE OF REVIEW: Machine learning (ML) and deep learning (DL) are two important categories of AI algorithms. Nowadays, AI technology has been gradually applied to cardiac magnetic resonance imaging (CMRI), covering the fields of myocardial contrast...

Temporally coherent cardiac motion tracking from cine MRI: Traditional registration method and modern CNN method.

Medical physics
PURPOSE: Cardiac motion tracking enables quantitative evaluation of myocardial strain, which is clinically interesting in cardiovascular disease research. However, motion tracking is difficult to perform manually. In this paper, we aim to develop and...

Fast and accurate calculation of myocardial T and T values using deep learning Bloch equation simulations (DeepBLESS).

Magnetic resonance in medicine
PURPOSE: To propose and evaluate a deep learning model for rapid and accurate calculation of myocardial T /T values based on a previously proposed Bloch equation simulation with slice profile correction (BLESSPC) method.

Cardiac MR segmentation based on sequence propagation by deep learning.

PloS one
Accurate segmentation of myocardial in cardiac MRI (magnetic resonance image) is key to effective rapid diagnosis and quantitative pathology analysis. However, a low-quality CMR (cardiac magnetic resonance) image with a large amount of noise makes it...

An integrated deep learning framework for joint segmentation of blood pool and myocardium.

Medical image analysis
Simultaneous and automatic segmentation of the blood pool and myocardium is an important precondition for early diagnosis and pre-operative planning in patients with complex congenital heart disease. However, due to the high diversity of cardiovascul...

Bioinspired Soft Robot with Incorporated Microelectrodes.

Journal of visualized experiments : JoVE
Bioinspired soft robotic systems that mimic living organisms using engineered muscle tissue and biomaterials are revolutionizing the current biorobotics paradigm, especially in biomedical research. Recreating artificial life-like actuation dynamics i...