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

Perspective-taking influences attentional deployment towards facial expressions of pain: An eye-tracking study

Observer eEmpathetic perspective-taking (PT) may be critical in modulating observer attention and associated caregiving responses to another’s pain. However, the differential effects of imagining oneself to be in the pain sufferer’s situation (‘Self-perspective’) or imagining the negative impacts on the pain sufferer’s experience (‘Other-perspective’) on attention have not been studied. The effects of observer PT (Self vs. Other) and level of facial pain expressiveness (FPE) upon attention to another person’s pain was investigated. Fifty-two adults were assigned to one of three PT conditions; they were instructed to view pairs of pain expressions and neutral faces and either 1) consider their own feelings (Self-perspective), 2) consider the feelings of the person in the picture (Other-perspective), or 3) received no further instructions (Control). Eye movements provided indices of early (probability and duration of first fixation) and later (total gaze duration) attentional deployment. Pain faces were more likely to be fixated upon first. A significant first fixation duration bias towards pain was observed, which increased with increasing levels of FPE, and was higher in the Self-PT than the Control condition. The proportion of total gaze duration on pain faces was higher in both experimental conditions than the Control condition. This effect was moderated by FPE in the Self-PT condition; there was a significant increase from low to high FPE. When observers attend to another’s facial display of pain, top-down influences (such as PT) and bottom-up influences (such as sufferer’s FPE) interact to control deployment and maintenance of attention

Long-Term Socio-Ecological Research in Practice: Lessons from Inter- and Transdisciplinary Research in the Austrian Eisenwurzen

Long-Term Socio-Ecological Research (LTSER) is an inter- and transdisciplinary research field addressing socio-ecological change over time at various spatial and temporal scales. In the Austrian Eisenwurzen region, an LTSER platform was founded in 2004. It has fostered and documented research projects aiming at advancing LTSER scientifically and at providing regional stakeholders with relevant information for sustainable regional development. Since its establishment, a broad range of research activities has been pursued in the region, integrating information from long-term ecological monitoring sites with approaches from social sciences and the humanities, and in cooperation with regional stakeholders. Based on the experiences gained in the Eisenwurzen LTSER platform, this article presents current activities in the heterogeneous field of LTSER, identifying specific (inter-)disciplinary contributions of three research strands of LTSER: long-term ecological research, socio-ecological basic research, and transdisciplinary research. Given the broad array of diverse contributions to LTSER, we argue that the platform has become a relevant "boundary organization", linking research to its regional non-academic context, and ensuring interdisciplinary exchange among the variety of disciplines. We consider the diversity of LTSER approaches an important resource for future research. Major success criteria of LTSER face specific challenges: (1) existing loose, yet stable networks need to be maintained and extended; (2) continuous generation of and access to relevant data needs to be secured and more data need to be included; and (3) consecutive research projects that have allowed for capacity building in the past may be threatened in the future if national Austrian research funders cease to provide resources

Migräne im Kindes- und Jugendalter — Ausblick auf innovative Behandlungsansätze im Rahmen multimodaler Therapiekonzepte

Although migraine is a~relevant health issue in children and adolescents, clinical care and research are still underrepresented and underfunded in this field. Quality of life can be significantly reduced when living with frequent episodes of pain. Due to the high level of vulnerability of the developing brain during adolescence, the risk of chronification and persistence into adulthood is high. In this narrative review, we describe the corner stones of a~patient-centered, multimodular treatment regimen. Further, an update on the pathophysiology of migraine is given considering the concept of a~periodically oscillating functional state of the brain in migraine patients (\textquotedblmigraine is a~brain state\textquotedbl). Besides central mechanisms, muscular structures with the symptoms of muscular pain, tenderness, or myofascial trigger points play an important role. Against this background, the currently available nonpharmacological and innovative neuromodulating approaches are presented focusing on the method of repetitive peripheral magnetic stimulation.Die Migräne ist auch im Kindes- und Jugendalter ein häufiges, aber in klinischer Versorgung und Wissenschaft oft unterrepräsentiertes Krankheitsbild. Gerade im Kindes- und Jugendalter bestehen relevante Einschränkungen der Lebensqualität durch das (häufige) Schmerzerfahren. Bedingt durch die entwicklungsspezifisch hohe Vulnerabilität des adoleszenten Gehirns besteht ein hohes Chronifizierungs- und Persistenzrisiko bis ins Erwachsenenalter hinein. In diesem Beitrag werden die Bestandteile eines patientenzentrierten, multimodalen Therapiekonzepts dargestellt. Darüber hinaus werden die aktuellsten Erkenntnisse zu den pathophysiologischen Grundlagen der Migräneerkrankung beleuchtet, nach denen Migräne durch einen sich phasenweise verändernden Funktionszustand des Gehirns entsteht (Stichwort: „migraine is a brain state“). Auch periphere Komponenten wie Muskelschmerzen, -verspannungen und -triggerpunkte spielen eine wichtige Rolle. Vor diesem Hintergrund werden nichtpharmakologische innovative Therapieansätze vorgestellt, die auf dem Prinzip der Neuromodulation beruhen, mit Fokus auf der repetitiven peripheren Magnetstimulation

Exploring a new paradigm for the fetal anomaly ultrasound scan: Artificial intelligence in real time

Objective: Advances in artificial intelligence (AI) have demonstrated potential to improve medical diagnosis. We piloted the end-to-end automation of the mid-trimester screening ultrasound scan using AI-enabled tools.Methods: A prospective method comparison study was conducted. Participants had both standard and AI-assisted US scans performed. The AI tools automated image acquisition, biometric measurement, and report production. A feedback survey captured the sonographers' perceptions of scanning.Results: Twenty-three subjects were studied. The average time saving per scan was 7.62 min (34.7%) with the AI-assisted method (p < 0.0001). There was no difference in reporting time. There were no clinically significant differences in biometric measurements between the two methods. The AI tools saved a satisfactory view in 93% of the cases (four core views only), and 73% for the full 13 views, compared to 98% for both using the manual scan. Survey responses suggest that the AI tools helped sonographers to concentrate on image interpretation by removing disruptive tasks.Conclusion: Separating freehand scanning from image capture and measurement resulted in a faster scan and altered workflow. Removing repetitive tasks may allow more attention to be directed identifying fetal malformation. Further work is required to improve the image plane detection algorithm for use in real time

Common Limitations of Image Processing Metrics:A Picture Story

While the importance of automatic image analysis is continuously increasing, recent meta-research revealed major flaws with respect to algorithm validation. Performance metrics are particularly key for meaningful, objective, and transparent performance assessment and validation of the used automatic algorithms, but relatively little attention has been given to the practical pitfalls when using specific metrics for a given image analysis task. These are typically related to (1) the disregard of inherent metric properties, such as the behaviour in the presence of class imbalance or small target structures, (2) the disregard of inherent data set properties, such as the non-independence of the test cases, and (3) the disregard of the actual biomedical domain interest that the metrics should reflect. This living dynamically document has the purpose to illustrate important limitations of performance metrics commonly applied in the field of image analysis. In this context, it focuses on biomedical image analysis problems that can be phrased as image-level classification, semantic segmentation, instance segmentation, or object detection task. The current version is based on a Delphi process on metrics conducted by an international consortium of image analysis experts from more than 60 institutions worldwide.Comment: This is a dynamic paper on limitations of commonly used metrics. The current version discusses metrics for image-level classification, semantic segmentation, object detection and instance segmentation. For missing use cases, comments or questions, please contact [email protected] or [email protected]. Substantial contributions to this document will be acknowledged with a co-authorshi

Understanding metric-related pitfalls in image analysis validation

Validation metrics are key for the reliable tracking of scientific progress and for bridging the current chasm between artificial intelligence (AI) research and its translation into practice. However, increasing evidence shows that particularly in image analysis, metrics are often chosen inadequately in relation to the underlying research problem. This could be attributed to a lack of accessibility of metric-related knowledge: While taking into account the individual strengths, weaknesses, and limitations of validation metrics is a critical prerequisite to making educated choices, the relevant knowledge is currently scattered and poorly accessible to individual researchers. Based on a multi-stage Delphi process conducted by a multidisciplinary expert consortium as well as extensive community feedback, the present work provides the first reliable and comprehensive common point of access to information on pitfalls related to validation metrics in image analysis. Focusing on biomedical image analysis but with the potential of transfer to other fields, the addressed pitfalls generalize across application domains and are categorized according to a newly created, domain-agnostic taxonomy. To facilitate comprehension, illustrations and specific examples accompany each pitfall. As a structured body of information accessible to researchers of all levels of expertise, this work enhances global comprehension of a key topic in image analysis validation.Comment: Shared first authors: Annika Reinke, Minu D. Tizabi; shared senior authors: Paul F. J\"ager, Lena Maier-Hei

Fast fetal head compounding from multi-view 3D ultrasound

The diagnostic value of ultrasound images may be limited by the presence of artefacts, notably acoustic shadows, lack of contrast and localised signal dropout. Some of these artefacts are dependent on probe orientation and scan technique, with each image giving a distinct, partial view of the imaged anatomy. In this work, we propose a novel method to fuse the partially imaged fetal head anatomy, acquired from numerous views, into a single coherent 3D volume of the full anatomy. Firstly, a stream of freehand 3D US images is acquired using a single probe, capturing as many different views of the head as possible. The imaged anatomy at each time-point is then independently aligned to a canonical pose using a recurrent spatial transformer network, making our approach robust to fast fetal and probe motion. Secondly, images are fused by averaging only the most consistent and salient features from all images, producing a more detailed compounding, while minimising artefacts. We evaluated our method quantitatively and qualitatively, using image quality metrics and expert ratings, yielding state of the art performance in terms of image quality and robustness to misalignments. Being online, fast and fully automated, our method shows promise for clinical use and deployment as a real-time tool in the fetal screening clinic, where it may enable unparallelled insight into the shape and structure of the face, skull and brain.</p