SonoNet: Real-Time Detection and Localisation of Fetal Standard Scan Planes in Freehand Ultrasound

Identifying and interpreting fetal standard scan planes during 2D ultrasound mid-pregnancy examinations are highly complex tasks which require years of training. Apart from guiding the probe to the correct location, it can be equally difficult for a non-expert to identify relevant structures within the image. Automatic image processing can provide tools to help experienced as well as inexperienced operators with these tasks. In this paper, we propose a novel method based on convolutional neural networks which can automatically detect 13 fetal standard views in freehand 2D ultrasound data as well as provide a localisation of the fetal structures via a bounding box. An important contribution is that the network learns to localise the target anatomy using weak supervision based on image-level labels only. The network architecture is designed to operate in real-time while providing optimal output for the localisation task. We present results for real-time annotation, retrospective frame retrieval from saved videos, and localisation on a very large and challenging dataset consisting of images and video recordings of full clinical anomaly screenings. We found that the proposed method achieved an average F1-score of 0.798 in a realistic classification experiment modelling real-time detection, and obtained a 90.09% accuracy for retrospective frame retrieval. Moreover, an accuracy of 77.8% was achieved on the localisation task.Comment: 12 pages, 8 figures, published in IEEE Transactions in Medical Imagin

Learning under Distributed Weak Supervision

The availability of training data for supervision is a frequently encountered bottleneck of medical image analysis methods. While typically established by a clinical expert rater, the increase in acquired imaging data renders traditional pixel-wise segmentations less feasible. In this paper, we examine the use of a crowdsourcing platform for the distribution of super-pixel weak annotation tasks and collect such annotations from a crowd of non-expert raters. The crowd annotations are subsequently used for training a fully convolutional neural network to address the problem of fetal brain segmentation in T2-weighted MR images. Using this approach we report encouraging results compared to highly targeted, fully supervised methods and potentially address a frequent problem impeding image analysis research

PVR: Patch-to-Volume Reconstruction for Large Area Motion Correction of Fetal MRI

In this paper we present a novel method for the correction of motion artifacts that are present in fetal Magnetic Resonance Imaging (MRI) scans of the whole uterus. Contrary to current slice-to-volume registration (SVR) methods, requiring an inflexible anatomical enclosure of a single investigated organ, the proposed patch-to-volume reconstruction (PVR) approach is able to reconstruct a large field of view of non-rigidly deforming structures. It relaxes rigid motion assumptions by introducing a specific amount of redundant information that is exploited with parallelized patch-wise optimization, super-resolution, and automatic outlier rejection. We further describe and provide an efficient parallel implementation of PVR allowing its execution within reasonable time on commercially available graphics processing units (GPU), enabling its use in the clinical practice. We evaluate PVR's computational overhead compared to standard methods and observe improved reconstruction accuracy in presence of affine motion artifacts of approximately 30% compared to conventional SVR in synthetic experiments. Furthermore, we have evaluated our method qualitatively and quantitatively on real fetal MRI data subject to maternal breathing and sudden fetal movements. We evaluate peak-signal-to-noise ratio (PSNR), structural similarity index (SSIM), and cross correlation (CC) with respect to the originally acquired data and provide a method for visual inspection of reconstruction uncertainty. With these experiments we demonstrate successful application of PVR motion compensation to the whole uterus, the human fetus, and the human placenta.Comment: 10 pages, 13 figures, submitted to IEEE Transactions on Medical Imaging. v2: wadded funders acknowledgements to preprin

Developing and validating a new scale to measure the acceptability of health apps among adolescents

Background The acceptability of health interventions is centrally important to achieving their desired health outcomes. The construct of acceptability of mobile health interventions among adolescents is neither well-defined nor consistently operationalized. Objectives Building on the theoretical framework of acceptability, these two studies developed and assessed the reliability and validity of a new scale to measure the acceptability of mobile health applications (“apps”) among adolescents. Methods We followed a structured scale development process including exploratory factor analyses (EFAs), confirmatory factor analyses (CFAs), and employed structural equation modeling (SEM) to assess the relationship between the scale and app usage. Adolescent participants used the Fooducate healthy eating app and completed the acceptability scale at baseline and one-week follow-up. Results EFA (n = 182) determined that the acceptability of health apps was a multidimensional construct with six latent factors: affective attitude, burden, ethicality, intervention coherence, perceived effectiveness, and self-efficacy. CFA (n = 161) from the second sample affirmed the six-factor structure and the unidimensional structures for each of the six subscales. However, CFA did not confirm the higher-order latent factor model suggesting that the six subscales reflect unique aspects of acceptability. SEM indicated that two of the subscales—ethicality and self-efficacy—were predictive of health app usage at one-week follow-up. Conclusions These results highlight the importance of ethicality and self-efficacy for health app acceptability. Future research testing and adapting this new acceptability scale will enhance measurement tools in the fields of mobile health and adolescent health

The neurodevelopmental implications of hypoplastic left heart syndrome in the fetus

Abstract As survival after cardiac surgery continues to improve, an increasing number of patients with hypoplastic left heart syndrome are reaching school age and beyond, with growing recognition of the wide range of neurodevelopmental challenges many survivors face. Improvements in fetal detection rates, coupled with advances in fetal ultrasound and MRI imaging, are contributing to a growing body of evidence that abnormal brain architecture is in fact present before birth in hypoplastic left heart syndrome patients, rather than being solely attributable to postnatal factors. We present an overview of the contemporary data on neurodevelopmental outcomes in hypoplastic left heart syndrome, focussing on imaging techniques that are providing greater insight into the nature of disruptions to the fetal circulation, alterations in cerebral blood flow and substrate delivery, disordered brain development, and an increased potential for neurological injury. These susceptibilities are present before any intervention, and are almost certainly substantial contributors to adverse neurodevelopmental outcomes in later childhood. The task now is to determine which subgroups of patients with hypoplastic left heart syndrome are at particular risk of poor neurodevelopmental outcomes and how that risk might be modified. This will allow for more comprehensive counselling for carers, better-informed decision making before birth, and earlier, more tailored provision of neuroprotective strategies and developmental support in the postnatal period

Temperature measurement on neurological pulse generators during MR scans

According to manufacturers of both magnetic resonance imaging (MRI) machines, and implantable neurological pulse generators (IPGs), MRI is contraindicated for patients with IPGs. A major argument for this restriction is the risk to induce heat in the leads due to the electromagnetic field, which could be dangerous for the surrounding brain parenchyma. The temperature change on the surface of the case of an ITREL-III (Medtronic Inc., Minneapolis, MN) and the lead tip during MRI was determined. An anatomical realistic and a cubic phantom, filled with phantom material mimicking human tissue, and a typical lead configuration were used to imitate a patient who carries an IPG for deep brain stimulation. The measurements were performed in a 1.5 T and a 3.0 T MRI. 2.1°C temperature increases at the lead tip uncovered the lead tip as the most critical part concerning heating problems in IPGs. Temperature increases in other locations were low compared to the one at the lead tip. The measured temperature increase of 2.1°C can not be considered as harmful to the patient. Comparison with the results of other studies revealed the avoidance of loops as a practical method to reduce heating during MRI procedures