EchoFusion: Tracking and Reconstruction of Objects in 4D Freehand Ultrasound Imaging without External Trackers

Ultrasound (US) is the most widely used fetal imaging technique. However, US images have limited capture range, and suffer from view dependent artefacts such as acoustic shadows. Compounding of overlapping 3D US acquisitions into a high-resolution volume can extend the field of view and remove image artefacts, which is useful for retrospective analysis including population based studies. However, such volume reconstructions require information about relative transformations between probe positions from which the individual volumes were acquired. In prenatal US scans, the fetus can move independently from the mother, making external trackers such as electromagnetic or optical tracking unable to track the motion between probe position and the moving fetus. We provide a novel methodology for image-based tracking and volume reconstruction by combining recent advances in deep learning and simultaneous localisation and mapping (SLAM). Tracking semantics are established through the use of a Residual 3D U-Net and the output is fed to the SLAM algorithm. As a proof of concept, experiments are conducted on US volumes taken from a whole body fetal phantom, and from the heads of real fetuses. For the fetal head segmentation, we also introduce a novel weak annotation approach to minimise the required manual effort for ground truth annotation. We evaluate our method qualitatively, and quantitatively with respect to tissue discrimination accuracy and tracking robustness.Comment: MICCAI Workshop on Perinatal, Preterm and Paediatric Image analysis (PIPPI), 201

Learn2Reg: comprehensive multi-task medical image registration challenge, dataset and evaluation in the era of deep learning

Image registration is a fundamental medical image analysis task, and a wide variety of approaches have been proposed. However, only a few studies have comprehensively compared medical image registration approaches on a wide range of clinically relevant tasks. This limits the development of registration methods, the adoption of research advances into practice, and a fair benchmark across competing approaches. The Learn2Reg challenge addresses these limitations by providing a multi-task medical image registration data set for comprehensive characterisation of deformable registration algorithms. A continuous evaluation will be possible at https://learn2reg.grand-challenge.org. Learn2Reg covers a wide range of anatomies (brain, abdomen, and thorax), modalities (ultrasound, CT, MR), availability of annotations, as well as intra- and inter-patient registration evaluation. We established an easily accessible framework for training and validation of 3D registration methods, which enabled the compilation of results of over 65 individual method submissions from more than 20 unique teams. We used a complementary set of metrics, including robustness, accuracy, plausibility, and runtime, enabling unique insight into the current state-of-the-art of medical image registration. This paper describes datasets, tasks, evaluation methods and results of the challenge, as well as results of further analysis of transferability to new datasets, the importance of label supervision, and resulting bias. While no single approach worked best across all tasks, many methodological aspects could be identified that push the performance of medical image registration to new state-of-the-art performance. Furthermore, we demystified the common belief that conventional registration methods have to be much slower than deep-learning-based methods

A global experiment on motivating social distancing during the COVID-19 pandemic

Finding communication strategies that effectively motivate social distancing continues to be a global public health priority during the COVID-19 pandemic. This cross-country, preregistered experiment (n = 25,718 from 89 countries) tested hypotheses concerning generalizable positive and negative outcomes of social distancing messages that promoted personal agency and reflective choices (i.e., an autonomy-supportive message) or were restrictive and shaming (i.e., a controlling message) compared with no message at all. Results partially supported experimental hypotheses in that the controlling message increased controlled motivation (a poorly internalized form of motivation relying on shame, guilt, and fear of social consequences) relative to no message. On the other hand, the autonomy-supportive message lowered feelings of defiance compared with the controlling message, but the controlling message did not differ from receiving no message at all. Unexpectedly, messages did not influence autonomous motivation (a highly internalized form of motivation relying on one’s core values) or behavioral intentions. Results supported hypothesized associations between people’s existing autonomous and controlled motivations and self-reported behavioral intentions to engage in social distancing. Controlled motivation was associated with more defiance and less long-term behavioral intention to engage in social distancing, whereas autonomous motivation was associated with less defiance and more short- and long-term intentions to social distance. Overall, this work highlights the potential harm of using shaming and pressuring language in public health communication, with implications for the current and future global health challenges

A multi-country test of brief reappraisal interventions on emotions during the COVID-19 pandemic.

The COVID-19 pandemic has increased negative emotions and decreased positive emotions globally. Left unchecked, these emotional changes might have a wide array of adverse impacts. To reduce negative emotions and increase positive emotions, we tested the effectiveness of reappraisal, an emotion-regulation strategy that modifies how one thinks about a situation. Participants from 87 countries and regions (n = 21,644) were randomly assigned to one of two brief reappraisal interventions (reconstrual or repurposing) or one of two control conditions (active or passive). Results revealed that both reappraisal interventions (vesus both control conditions) consistently reduced negative emotions and increased positive emotions across different measures. Reconstrual and repurposing interventions had similar effects. Importantly, planned exploratory analyses indicated that reappraisal interventions did not reduce intentions to practice preventive health behaviours. The findings demonstrate the viability of creating scalable, low-cost interventions for use around the world

Bayesian and deep generative modelling for image registration, with a focus on uncertainty quantification & similarity learning

Image registration is the process of aligning images into a common coordinate system where discrete pixel locations represent the same semantic information. Beside object detection and image segmentation, image registration is the backbone of many image analysis pipelines. However, practical use of automated image registration is limited by accuracy, which remains unsatisfactory except in case of relatively simple problems, e.g. rigid image registration, where only a single linear transformation applied to an image. In this thesis we present new methods to improve on the existing algorithms for non-rigid image registration, which models local differences between images. Firstly, we formulate a Bayesian model for image registration that overcomes the existing barriers to uncertainty quantification when using a dense, high-dimensional, and diffeomorphic transformation parametrisation, and use stochastic gradient Markov chain Monte Carlo to quantify image registration uncertainty on large, three-dimensional images. Secondly, we develop a variational Bayesian method for diffeomorphic, non-rigid registration of medical images. This model learns in an unsupervised way a data-specific similarity metric for mono-modal atlas-based image registration, i.e. when all the images in the dataset are aligned to a single target image. The similarity metric is parametrised as a neural network and leads to more accurate results than traditional similarity metrics which are used to initialise the model, e.g. sum of squared differences and local cross-correlation. The proposed approach has little to no impact on image registration speed and transformation smoothness. Finally, we formulate unsupervised similarity learning as a maximum likelihood estimation problem, with the similarity metric parametrised as an energy-based model. This formulation simplifies the model and enables us to make use of larger datasets in order to further improve unsupervised image registration accuracy also in case of pairwise image registration, i.e. when using any image in the dataset as the target image.Open Acces

Laser in Pediatric Dentistry

Laser technology has different applications in dentistry, and, particularly, in Paediatric Dentistry. Depending on laser wavelengths and the physical properties of the tissue which is to be targeted; it is possible obtain different results in three main dental fields: Diagnosis, Prevention and Operative Therapy. Conventional treatments can sometimes be replaced by laser treatments and better results may be achieved. Laser treatments offer new treatment opportunities in the dental field that were unknown in the past. This chapter aims to outline the clinical protocols and possible applications of different laser systems in Paediatric Dentistry

Author correction: A multi-country test of brief reappraisal interventions on emotions during the COVID-19 pandemic

Correction to: Nature Human Behaviour https://doi.org/10.1038/s41562-021-01173-x, published online 2 August 2021. In the version of this article initially published, the following authors were omitted from the author list and the Author contributionssection for “investigation” and “writing and editing”: Nandor Hajdu (Institute of Psychology, ELTE Eötvös Loránd University, Budapest,Hungary), Jordane Boudesseul (Facultad de Psicología, Instituto de Investigación Científica, Universidad de Lima, Lima, Perú), RafałMuda (Faculty of Economics, Maria Curie-Sklodowska University, Lublin, Poland) and Sandersan Onie (Black Dog Institute, UNSWSydney, Sydney, Australia & Emotional Health for All Foundation, Jakarta, Indonesia). In addition, Saeideh FatahModares’ name wasoriginally misspelled as Saiedeh FatahModarres in the author list. Further, affiliations have been corrected for Maria Terskova (NationalResearch University Higher School of Economics, Moscow, Russia), Susana Ruiz Fernandez (FOM University of Applied Sciences,Essen; Leibniz-Institut fur Wissensmedien, Tubingen, and LEAD Research Network, Eberhard Karls University, Tubingen, Germany),Hendrik Godbersen (FOM University of Applied Sciences, Essen, Germany), Gulnaz Anjum (Department of Psychology, Simon FraserUniversity, Burnaby, Canada, and Department of Economics & Social Sciences, Institute of Business Administration, Karachi, Pakistan)

Author Correction: A multi-country test of brief reappraisal interventions on emotions during the COVID-19 pandemic.

Correction to: Nature Human Behaviour https://doi.org/10.1038/s41562-021-01173-x, published online 2 August 2021. In the version of this article initially published, the following authors were omitted from the author list and the Author contributionssection for “investigation” and “writing and editing”: Nandor Hajdu (Institute of Psychology, ELTE Eötvös Loránd University, Budapest,Hungary), Jordane Boudesseul (Facultad de Psicología, Instituto de Investigación Científica, Universidad de Lima, Lima, Perú), RafałMuda (Faculty of Economics, Maria Curie-Sklodowska University, Lublin, Poland) and Sandersan Onie (Black Dog Institute, UNSWSydney, Sydney, Australia & Emotional Health for All Foundation, Jakarta, Indonesia). In addition, Saeideh FatahModares’ name wasoriginally misspelled as Saiedeh FatahModarres in the author list. Further, affiliations have been corrected for Maria Terskova (NationalResearch University Higher School of Economics, Moscow, Russia), Susana Ruiz Fernandez (FOM University of Applied Sciences,Essen; Leibniz-Institut fur Wissensmedien, Tubingen, and LEAD Research Network, Eberhard Karls University, Tubingen, Germany),Hendrik Godbersen (FOM University of Applied Sciences, Essen, Germany), Gulnaz Anjum (Department of Psychology, Simon FraserUniversity, Burnaby, Canada, and Department of Economics & Social Sciences, Institute of Business Administration, Karachi, Pakistan)

A global experiment on motivating social distancing during the COVID-19 pandemic

Finding communication strategies that effectively motivate social distancing continues to be a global public health priority during the COVID-19 pandemic. This cross-country, preregistered experiment (n = 25,718 from 89 countries) tested hypotheses concerning generalizable positive and negative outcomes of social distancing messages that promoted personal agency and reflective choices (i.e., an autonomy-supportive message) or were restrictive and shaming (i.e., a controlling message) compared with no message at all. Results partially supported experimental hypotheses in that the controlling message increased controlled motivation (a poorly internalized form of motivation relying on shame, guilt, and fear of social consequences) relative to no message. On the other hand, the autonomy-supportive message lowered feelings of defiance compared with the controlling message, but the controlling message did not differ from receiving no message at all. Unexpectedly, messages did not influence autonomous motivation (a highly internalized form of motivation relying on one’s core values) or behavioral intentions. Results supported hypothesized associations between people’s existing autonomous and controlled motivations and self-reported behavioral intentions to engage in social distancing. Controlled motivation was associated with more defiance and less long-term behavioral intention to engage in social distancing, whereas autonomous motivation was associated with less defiance and more short- and long-term intentions to social distance. Overall, this work highlights the potential harm of using shaming and pressuring language in public health communication, with implications for the current and future global health challenges