MOUSIE · Multi-Organ UltraSound-based Inborn Evaluation
Horizon 2020 — Marie Skłodowska-Curie Actions
- Duration
- 2016-11-01 → 2018-10-31
- EU contribution
- €183,455
- Participants
- 1
- Scheme
- MSCA-IF-EF-ST
Lines connect the coordinator with its partners.
Results in brief
Multi-Organ UltraSound-based Inborn Evaluation
Two-dimensional (2D) ultrasound (US) screening is the preferred imaging modality for prenatal evaluation for growth, gestational age estimation, and early structural abnormalities detection. However, prenatal screening still relies on 2D measurements of different organs that are analysed separately from organ-specific standard planes, manually acquired using a hand-held prove, thus suffering from subjectivity, low reproducibility, and operator dependency. The main purpose of the MOUSIE project was to develop new image-based machine learning solutions for a more efficient, quantitative and accurate prenatal healthcare. Thanks to the recent progress in deep learning techniques, it is now possible to conceive image analysis frameworks that operate on US data alone, able to analyse the rich and dense information of this modality in a more efficient and accurate way. The shift of the machine learning community towards these deep learning-based solutions, and the great potential of this technology in the medical image analysis field, were early identified in the project, thus adapting the research plan accordingly. The work carried out in this project has revolved around two main fundamental pillars: i) the development of new machine learning solutions for the efficient and accurate analysis of fetal sonographic images; and ii) the use of 3D US volumetric data to mitigate the inherent practical and diagnostic limitations of traditional 2D sonographic scans.
Data: CORDIS, © European Union
Project objective
Prenatal diagnosis of congenital abnormalities and intrauterine growth restriction (IGR) has become increasingly important thanks to the recent advancements in obstetrical ultrasound (US) imaging. An accurate and early diagnosis of fetal malformations and growth anomalies can improve fetal prognosis by allowing a treatment plan to be produced, enabling access to specialist units and appropriate treatments from birth. However, the diagnostic accuracy of US is limited due to its subjective assessment and inter-operator variability. On the other hand, magnetic resonance imaging (MRI) has the potential to offer a more detailed examination of the fetus. Unfortunately, its high cost, limited availability, and the difficulty in acquiring high quality 3D data due to constant fetal motion, hinder the widespread use of fetal MRI.The MOUSIE project aims at improving the accuracy of fetal US examination by creating the first framework for multi-organ quantitative image analysis of the fetus. In particular, the specific goals of the MOUSIE project are: (1) development of new multi-organ MRI slice to volume reconstruction method able to provide comprehensive and relevant 3D inter-organ information of the fetal anatomy, including spine, lungs, liver and kidneys; (2) development of an US-based automated segmentation method, using a detailed MRI-US atlas of the anatomy of the fetus of 18-20 weeks of gestational age, that includes new structural similarity patterns and inter-organ shape models; (3) creation of a new generation of multi-organ fetal biomarkers based on the detailed and comprehensive anatomical information extracted from a unique database with more than 500 MRI-US scans, including healthy and pathological cases. By achieving these goals, MOUSIE will provide an innovative set of methods allowing for the first time quantitative, noninvasive, objective assessment of the fetal anatomy and growth, and thus address a long standing clinical need for such methodology.
Original text from CORDIS.
Participants
- IMPERIAL COLLEGE OF SCIENCE TECHNOLOGY AND MEDICINE · LondonCoordinatorUnited Kingdom
Links
- View on CORDIS
- DOI: 10.3030/706372
- https://arquivo.pt/wayback/20201229133327/http://juanjosecerrolaza.com/
Data: CORDIS, © European Union