Real-time standard scan plane detection and localisation in fetal ultrasound using fully convolutional neural networks

Fetal mid-pregnancy scans are typically carried out according to fixed protocols. Accurate detection of abnormalities and correct biometric measurements hinge on the correct acquisition of clearly defined standard scan planes. Locating these standard planes requires a high level of expertise. However, there is a worldwide shortage of expert sonographers. In this paper, we consider a fully automated system based on convolutional neural networks which can detect twelve standard scan planes as defined by the UK fetal abnormality screening programme. The network design allows real-time inference and can be naturally extended to provide an approximate localisation of the fetal anatomy in the image. Such a framework can be used to automate or assist with scan plane selection, or for the retrospective retrieval of scan planes from recorded videos. The method is evaluated on a large database of 1003 volunteer mid-pregnancy scans. We show that standard planes acquired in a clinical scenario are robustly detected with a precision and recall of 69 % and 80 %, which is superior to the current state-of-the-art. Furthermore, we show that it can retrospectively retrieve correct scan planes with an accuracy of 71 % for cardiac views and 81 % for non-cardiac views

The cost of changing physical activity behaviour: Evidence from a "physical activity pathway" in the primary care setting

Copyright @ 2011 Boehler et al.BACKGROUND: The ‘Physical Activity Care Pathway’ (a Pilot for the ‘Let’s Get Moving’ policy) is a systematic approach to integrating physical activity promotion into the primary care setting. It combines several methods reported to support behavioural change, including brief interventions, motivational interviewing, goal setting, providing written resources, and follow-up support. This paper compares costs falling on the UK National Health Service (NHS) of implementing the care pathway using two different recruitment strategies and provides initial insights into the cost of changing physical activity behaviour. METHODS: A combination of a time driven variant of activity based costing, audit data through EMIS and a survey of practice managers provided patient-level cost data for 411 screened individuals. Self reported physical activity data of 70 people completing the care pathway at three month was compared with baseline using a regression based ‘difference in differences’ approach. Deterministic and probabilistic sensitivity analyses in combination with hypothesis testing were used to judge how robust findings are to key assumptions and to assess the uncertainty around estimates of the cost of changing physical activity behaviour. RESULTS: It cost £53 (SD 7.8) per patient completing the PACP in opportunistic centres and £191 (SD 39) at disease register sites. The completer rate was higher in disease register centres (27.3% vs. 16.2%) and the difference in differences in time spent on physical activity was 81.32 (SE 17.16) minutes/week in patients completing the PACP; so that the incremental cost of converting one sedentary adult to an ‘active state’ of 150 minutes of moderate intensity physical activity per week amounts to £ 886.50 in disease register practices, compared to opportunistic screening. CONCLUSIONS: Disease register screening is more costly than opportunistic patient recruitment. However, additional costs come with a higher completion rate and better outcomes in terms of behavioural change in patients completing the care pathway. Further research is needed to rigorously evaluate intervention efficiency and to assess the link between behavioural change and changes in quality adjusted life years (QALYs).This article is available through the Brunel Open Access Publishing Fund