Spontaneous polarization and rotational viscosity measurements on ferroelectric liquid crystals derived from trans-p-n-alkoxycinnamic acids

We report here the results of systematic measurements of spontaneous polarization and rotational viscosity on four structurally related homologous series derived from trans-p-n-alkoxycinnamic acids. The influence of alkyl chain length on the magnitude and temperature dependence of the polarization, and the effect of subtle structural changes on the rotational viscosity are discussed

Learning Shape Priors for Robust Cardiac MR Segmentation from Multi-view Images

© 2019, Springer Nature Switzerland AG. Cardiac MR image segmentation is essential for the morphological and functional analysis of the heart. Inspired by how experienced clinicians assess the cardiac morphology and function across multiple standard views (i.e. long- and short-axis views), we propose a novel approach which learns anatomical shape priors across different 2D standard views and leverages these priors to segment the left ventricular (LV) myocardium from short-axis MR image stacks. The proposed segmentation method has the advantage of being a 2D network but at the same time incorporates spatial context from multiple, complementary views that span a 3D space. Our method achieves accurate and robust segmentation of the myocardium across different short-axis slices (from apex to base), outperforming baseline models (e.g. 2D U-Net, 3D U-Net) while achieving higher data efficiency. Compared to the 2D U-Net, the proposed method reduces the mean Hausdorff distance (mm) from 3.24 to 2.49 on the apical slices, from 2.34 to 2.09 on the middle slices and from 3.62 to 2.76 on the basal slices on the test set, when only 10% of the training data was used

Deep Learning Techniques for Automatic MRI Cardiac Multi-Structures Segmentation and Diagnosis: Is the Problem Solved?

Delineation of the left ventricular cavity, myocardium, and right ventricle from cardiac magnetic resonance images (multi-slice 2-D cine MRI) is a common clinical task to establish diagnosis. The automation of the corresponding tasks has thus been the subject of intense research over the past decades. In this paper, we introduce the "Automatic Cardiac Diagnosis Challenge" dataset (ACDC), the largest publicly available and fully annotated dataset for the purpose of cardiac MRI (CMR) assessment. The dataset contains data from 150 multi-equipments CMRI recordings with reference measurements and classification from two medical experts. The overarching objective of this paper is to measure how far state-of-the-art deep learning methods can go at assessing CMRI, i.e., segmenting the myocardium and the two ventricles as well as classifying pathologies. In the wake of the 2017 MICCAI-ACDC challenge, we report results from deep learning methods provided by nine research groups for the segmentation task and four groups for the classification task. Results show that the best methods faithfully reproduce the expert analysis, leading to a mean value of 0.97 correlation score for the automatic extraction of clinical indices and an accuracy of 0.96 for automatic diagnosis. These results clearly open the door to highly accurate and fully automatic analysis of cardiac CMRI. We also identify scenarios for which deep learning methods are still failing. Both the dataset and detailed results are publicly available online, while the platform will remain open for new submissions

Century-Old Steam Lorry - Restored to its Pristine Elegance at NSCM

20-23While the lockdown due to the COVID-19 pandemic shut down museums throughout the world, the Nehru Science Centre Mumbai snatched the opportunity to restore a hundred-year-old steam engine and a major attraction with visitors, to its pristine glory

The High pressure studies on ferroelectric liquid crystals

We present here an overview of our high pressure investigations on ferroelectric liquid crystals. We begin with a brief description of the optical and electrical high pressure set up, followed by data highlighting the influence of pressure on a variety of properties. The results can be summarized as follows. At a constant relative temperature (with respect to the A-C<SUP>&#8727;</SUP> phase boundary) the spontaneous polarisation (P<SUB>s</SUB>) decreases linearly with increasing pressure, whereas the coercive field E<SUB>c</SUB>' the critical field E<SUB>u</SUB> for unwinding the helix and the rotational viscosity &#947;&#934; associated with ferroelectric switching increase linearly with pressure. The temperature and frequency dependence of the transverse dielectric constant show more dramatic changes. The dominant effect on the dielectric constant appears to be due to the slowing down of the director relaxation modes, viz., the Goldstone mode and the soft mode with pressure. The ferroelectric switching time estimated from the simple relation &#964; = &#947; /P<SUB>s</SUB> E shows a linear increase with pressure. Experiments are in progress to measure the pressure dependence of the pitch

Dielectric studies of Goldstone mode and soft mode in the vicinity of the A-C⋆^\star transition

We report the results of dielectric studies on three ferroelectric liquid crystals exhibiting the A-C$^\star$ transition. Two relaxation modes viz., the soft mode and the Goldstone mode have been observed in the frequency domain covered. Careful measurements have allowed us to observe the soft mode relaxation in the C$^\star$ phase, even in the absence of a bias field, over a larger temperature range than in any previous study. The results are discussed in the light of the predictions of the generalised mean-field model

Dielectric studies in the vicinity of the A-C<SUP>&#8727; </SUP> transition

We have studied the soft mode and Goldstone mode relaxations in a ferroelectric liquid crystal compound (referred to as C6 for short) exhibiting a high value of spontaneous polarisation. The results show that the relaxation frequency fs as well as the dielectric strength &#916;&#949;S can be described by a simple power law, but gives non-mean field exponents. However, fitting done to the &#916;&#949;s data using an expression given by the generalised mean field theory, not only yields the mean field susceptibility exponent gamma but also helps in determining the Landau coefficients a and C (where a is the electroclinic coefficient and C the polarisation-tilt coupling constant) as well as the soft mode viscosity. We also present the Goldstone mode viscosity and the relevant elastic torque data. Measurements on C7 and C8, the higher homologs of C6, bring out the effect of chain length on the relaxation parameters

Automatic Myocardial Scar Segmentation from Multi-sequence Cardiac MRI Using Fully Convolutional Densenet with Inception and Squeeze-Excitation Module

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