|
 Automatic Pipeline for Segmentation of LV Myocardium on Quantitative
MR T1 Maps using Deep Learning Model and Computation of Radial T1 and ECV
Values. Jafari, R., et al. NMR in Biomedicine (2024).
Native T1 mapping is
a non-invasive technique used for early detection of diffused myocardial
abnormalities, and it provides baseline tissue characterization.
Post-contrast T1 mapping enhances tissue differentiation, enables
extracellular volume (ECV) calculation, and improves myocardial viability
assessment. Accurate and precise segmenting of the left ventricular (LV)
myocardium on T1 maps is crucial for assessing myocardial tissue
characteristics and diagnosing cardiovascular diseases (CVD). This study presents
a deep learning (DL) based pipeline for automatically segmenting LV
myocardium on T1 maps and automatic computation of radial T1 and ECV
values.
|
|
 Automatic Multiclass Tissue Segmentation using
Deep Learning in Brain MR images of Tumor Patients. Kandpal A, et al.
(2024).
Precise
delineation of brain tissues, including lesions, in MR images is crucial
for data analysis and objectively assessing conditions like neurological
disorders and brain tumors. Existing methods for tissue segmentation often
fall short in addressing patients with lesions, particularly those with
brain tumors. This study aimed to develop and evaluate a robust pipeline
utilizing convolutional neural networks for rapid and automatic
segmentation of whole brain tissues, including tumor lesions. The proposed
pipeline Fig. Screenshot of 'Software Tool for Brain Tissue and Lesion
Segmentation
can accurately segment brain tissues and
tumor lesions
automatically and can be used as a
standalone software solution for research and clinical applications.
|
|
Deep
learning-based segmentation of left ventricular myocardium on dynamic
contrast-enhanced MRI…: Jafari, R., et al. Int J CARS (2024). https://doi.org/10.1007/s11548-024-03221-z
Cardiac
perfusion MRI is vital for disease diagnosis, treatment planning, and risk
stratification, with anomalies serving as markers of underlying ischemic
pathologies. This study proposed a fast and fully automated AI-assisted
method to segment LV myocardium on all timeframes of DCE-MRI data. The
method is robust, and its performance is independent of the intra-temporal
sequence registration and can easily accommodate timeframes with potential
registration errors.
|
|
Creatine Mapping
in Human Brain using CEST MRI: Singh A, et al. NMR Biomedicine (2019)
|
|
Brain Tumor Segmentation: Sengupta A, et al. EJR (2018)
|
|
Breast Tissue
Segmentation: Snekha, et al, PlosOne (2017)
|
|
Breast Cancer
Classification(2019)
|
|
MRI compatible
Knee Joint Loading Device (2018)
|
|
T1 Perfusion (DCE)
MRI Analysis Tool (2016-present)
|