Calculation of the Raman and IR frequencies as order parameters and the damping constant (FWHM) close to phase transitions in methylhydrazinium structures

Kurt, M.; Yurtseven, H.; Kurt, Arzu

doi:10.1016/j.molstruc.2019.01.016

Cited by 2 publications

(1 citation statement)

References 19 publications

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“…LGE-CMR is recognized as the “gold standard” non-invasive method for visualizing and diagnosing MI and also provides vital prognostic information following MI. Several methods are available for the quantitative assessment of MI size (from LGE images), including visual assessment, manual planimetry, and semi-quantitative methods (such as full width at half maximum [FWHM]) [ 6 , 7 ]. However, to date, there is no “gold standard” fully automatic method for MI detection and quantification.…”

Section: Introductionmentioning

confidence: 99%

MyI-Net: Fully Automatic Detection and Quantification of Myocardial Infarction from Cardiovascular MRI Images

Wang

Abdelaty

Parke

et al. 2023

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Myocardial infarction (MI) occurs when an artery supplying blood to the heart is abruptly occluded. The “gold standard” method for imaging MI is cardiovascular magnetic resonance imaging (MRI) with intravenously administered gadolinium-based contrast (with damaged areas apparent as late gadolinium enhancement [LGE]). However, no “gold standard” fully automated method for the quantification of MI exists. In this work, we propose an end-to-end fully automatic system (MyI-Net) for the detection and quantification of MI in MRI images. It has the potential to reduce uncertainty due to technical variability across labs and the inherent problems of data and labels. Our system consists of four processing stages designed to maintain the flow of information across scales. First, features from raw MRI images are generated using feature extractors built on ResNet and MoblieNet architectures. This is followed by atrous spatial pyramid pooling (ASPP) to produce spatial information at different scales to preserve more image context. High-level features from ASPP and initial low-level features are concatenated at the third stage and then passed to the fourth stage where spatial information is recovered via up-sampling to produce final image segmentation output into: (i) background, (ii) heart muscle, (iii) blood and (iv) LGE areas. Our experiments show that the model named MI-ResNet50-AC provides the best global accuracy (97.38%), mean accuracy (86.01%), weighted intersection over union (IoU) of 96.47%, and bfscore of 64.46% for the global segmentation. However, in detecting only LGE tissue, a smaller model, MI-ResNet18-AC, exhibited higher accuracy (74.41%) than MI-ResNet50-AC (64.29%). New models were compared with state-of-the-art models and manual quantification. Our models demonstrated favorable performance in global segmentation and LGE detection relative to the state-of-the-art, including a four-fold better performance in matching LGE pixels to contours produced by clinicians.

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Section: Introductionmentioning

confidence: 99%