Feature detection from echocardiography images using local phase information

Ultrasound images are characterized by their special speckle appearance, low contrast, and low signal-to-noise ratio. It is always challenging to extract important clinical information from these images. An important step before formal analysis is to transform the image to significant features of interest. Intensity based methods do not perform particularly well on ultrasound images. However, it has been previously shown that these images respond well to local phase-based methods which are theoretically intensity-invariant and thus suitable for ultrasound images. We extend the previous local phase-based method to detect features using the local phase computed from monogenic signal which is an isotropic extension of the analytic signal. We apply our method of multiscale feature-asymmetry measurement and local phase-gradient computation to cardiac ultrasound (echocardiography) images for the detection of endocardial, epicardial and myocardial centerline

Feature Tracking Cardiac Magnetic Resonance via Deep Learning and Spline Optimization

Feature tracking Cardiac Magnetic Resonance (CMR) has recently emerged as an area of interest for quantification of regional cardiac function from balanced, steady state free precession (SSFP) cine sequences. However, currently available techniques lack full automation, limiting reproducibility. We propose a fully automated technique whereby a CMR image sequence is first segmented with a deep, fully convolutional neural network (CNN) architecture, and quadratic basis splines are fitted simultaneously across all cardiac frames using least squares optimization. Experiments are performed using data from 42 patients with hypertrophic cardiomyopathy (HCM) and 21 healthy control subjects. In terms of segmentation, we compared state-of-the-art CNN frameworks, U-Net and dilated convolution architectures, with and without temporal context, using cross validation with three folds. Performance relative to expert manual segmentation was similar across all networks: pixel accuracy was ~97%, intersection-over-union (IoU) across all classes was ~87%, and IoU across foreground classes only was ~85%. Endocardial left ventricular circumferential strain calculated from the proposed pipeline was significantly different in control and disease subjects (-25.3% vs -29.1%, p = 0.006), in agreement with the current clinical literature.Comment: Accepted to Functional Imaging and Modeling of the Heart (FIMH) 201

Assembly and Characterization of SAMs Formed by the Adsorption of Alkanethiols on Zinc Selenide Substrates

Alkanethiols HS(CH2)nCH3 (n = 7, 11, 15, 17) and the hydroxy functional thiol HS(CH2)12OH are shown to adsorb from solution onto zinc selenide crystals and form well-organized monolayers. The chemisorption of these organosulfur compounds has been studied using transmission Fourier transform infrared spectroscopy (FTIR), X-ray photoelectron spectroscopy (XPS), atomic force microscopy (AFM), and surface wetting properties. FTIR indicates that the longer chain alkanethiols (n = 15, 17) form well-defined SAMs with crystalline-like conformations of the chains within the monolayers. As the chain length decreases, there is less conformational order present in the monolayers. The orientation of the largely trans-zigzag alkyl chains on the surface is at a slight tilt off the surface normal direction, as indicated by the dichroism of the infrared spectra and the chain-length-dependent scaling of the mass coverage of the SAM. The structural data obtained by XPS and FTIR are compatible with the inferences derived from measurements of surface wetting properties. The thickness of the SAM layers calculated using XPS is found to be comparable to the thickness of alkanethiol SAMs formed on gold and other metal surfaces. Changes in the surface of the ZnSe crystal because of chemisorption of the thiols are illustrated by data from AFM imaging. The feasibility of forming cohesive patterned monolayers by microcontact printing is also demonstrated

Controlled Clinical Trial of a Self-Help for Anxiety Intervention for Patients Waiting for Psychological Therapy

This study was a controlled clinical trial in which patients were offered a brief low cost, low intensity self-help intervention while waiting for psychological therapy. A CBT based self-help pack was given to patients with significant anxiety problems and no attempt was made to exclude patients on the basis of severity or co-morbidity. The treatment group received the intervention immediately following assessment and the control group after a delay of 8 weeks so comparisons between the two groups were made over 8 weeks. Although there was some support for the effectiveness of the self help intervention, with a significant time x group interaction for CORE-OM scores, this was not significant with the intention to treat analysis, nor for HADS anxiety and depression scores and the effect size was low. A follow up evaluation suggested some patients attributed significant goal attainment to the intervention. The findings suggest the routine use of self-help interventions in psychological therapies services should be considered although further more adequately powered research is required to identify the type of patients and problems that most benefit, possible adverse effects and the effect on subsequent uptake of and engagement in therapy

UPI-Net: Semantic Contour Detection in Placental Ultrasound

Semantic contour detection is a challenging problem that is often met in medical imaging, of which placental image analysis is a particular example. In this paper, we investigate utero-placental interface (UPI) detection in 2D placental ultrasound images by formulating it as a semantic contour detection problem. As opposed to natural images, placental ultrasound images contain specific anatomical structures thus have unique geometry. We argue it would be beneficial for UPI detectors to incorporate global context modelling in order to reduce unwanted false positive UPI predictions. Our approach, namely UPI-Net, aims to capture long-range dependencies in placenta geometry through lightweight global context modelling and effective multi-scale feature aggregation. We perform a subject-level 10-fold nested cross-validation on a placental ultrasound database (4,871 images with labelled UPI from 49 scans). Experimental results demonstrate that, without introducing considerable computational overhead, UPI-Net yields the highest performance in terms of standard contour detection metrics, compared to other competitive benchmarks.Comment: 9 pages, 8 figures, accepted at Visual Recognition for Medical Images (VRMI), ICCV 201

Rethinking Semi-Supervised Federated Learning: How to co-train fully-labeled and fully-unlabeled client imaging data

The most challenging, yet practical, setting of semi-supervised federated learning (SSFL) is where a few clients have fully labeled data whereas the other clients have fully unlabeled data. This is particularly common in healthcare settings where collaborating partners (typically hospitals) may have images but not annotations. The bottleneck in this setting is the joint training of labeled and unlabeled clients as the objective function for each client varies based on the availability of labels. This paper investigates an alternative way for effective training with labeled and unlabeled clients in a federated setting. We propose a novel learning scheme specifically designed for SSFL which we call Isolated Federated Learning (IsoFed) that circumvents the problem by avoiding simple averaging of supervised and semi-supervised models together. In particular, our training approach consists of two parts - (a) isolated aggregation of labeled and unlabeled client models, and (b) local self-supervised pretraining of isolated global models in all clients. We evaluate our model performance on medical image datasets of four different modalities publicly available within the biomedical image classification benchmark MedMNIST. We further vary the proportion of labeled clients and the degree of heterogeneity to demonstrate the effectiveness of the proposed method under varied experimental settings.Comment: Published in MICCAI 2023 with early acceptance and selected as 1 of the top 20 poster highlights under the category: Which work has the potential to impact other applications of AI and C

Effects of Surface Morphology on the Anchoring and Electrooptical Dynamics of Confined Nanoscale Liquid Crystalline Films

The orientation and dynamics of two 40-nm thick films of 4-n-pentyl-4‘-cyanobiphenyl (5CB), a nematic liquid crystal, have been studied using step-scan Fourier transform infrared spectroscopy (FTIR). The films are confined in nanocavities bounded by an interdigitated electrode array (IDA) patterned on a zinc selenide (ZnSe) substrate. The effects of the ZnSe surface morphology (specifically, two variations of nanometer-scale corrugations obtained by mechanical polishing) on the initial ordering and reorientation dynamics of the electric-field-induced Freedericksz transition are presented here. The interaction of the 5CB with ZnSe surfaces bearing a spicular corrugation induces a homeotropic (surface normal) alignment of the film confined in the cavity. Alternately, when ZnSe is polished to generate fine grooves along the surface, a planar alignment is promoted in the liquid crystalline film. Time-resolved FTIR studies that enable the direct measurement of the rate constants for the electric-field-induced orientation and thermal relaxation reveal that the dynamic transitions of the two film structures are significantly different. These measurements quantitatively demonstrate the strong effects of surface morphology on the anchoring, order, and dynamics of liquid crystalline thin films