Projektbeschreibung
Mit Deep Learning die medizinische Bildgebung optimieren
Dank der medizinischen Bildgebung konnten Diagnose, Behandlung und Nachsorge revolutioniert werden. Außerdem liefert sie mit sehr hoher räumlicher Auflösung grundlegende Informationen über Anatomie und Physiologie. Das bildgebende Verfahren kann jedoch für die Patientinnen und Patienten eine Belastung darstellen, und kommt Bewegung mit ins Spiel, dann wird es schwierig. Hauptziel des EU-finanzierten Projekts Deep4MI ist daher die Weiterentwicklung und Automatisierung der medizinischen Bildgebung, um im Sinne der klinischen Entscheidungsfindung zu höherer diagnostischer und prognostischer Genauigkeit zu gelangen. Das Forschungsteam wird unter Einsatz von Verfahren des maschinellen Lernens und von Deep Learning die Bilderfassung, -rekonstruktion und -analyse verbessern, um aus medizinischem Bildmaterial mehr klinische Informationen zu gewinnen und die Ergebnisinterpretation zu optimieren.
Ziel
Medical imaging has revolutionized medicine and healthcare like no other recent technology, and is now an integral part of diagnosis, treatment planning, treatment delivery and follow-up. It provides an unparalleled ability to image anatomy and function with high spatial (and temporal) resolution. Its success has led to a dramatic increase in the number of medical imaging examinations. Despite this success, medical imaging is often stressful for patients, requires patient cooperation and is difficult in the presence of motion (e.g. patient motion or breathing motion). Furthermore, even more than 100 years after the discovery of X-rays, the interpretation of medical images relies almost exclusively on human experts. All of the above mean that there is a strong need for increased automation and quantification in order to reduce costs, increase efficiency and patient-friendliness, and provide higher diagnostic and prognostic accuracy for clinical decision making.
At the same time, machine learning and deep learning techniques have made significant advances and have started to make a large impact in many real-world applications. The aim of this proposal is to exploit these advances to address the above challenges and to achieve a paradigm shift in the way information is extracted from medical images for diagnostics, therapy and follow-up. We will do this by developing a transformative and synergistic approach to medical imaging in which acquisition, reconstruction, analysis and interpretation will be tightly coupled, with bidirectional feedback between the different stages, in order to optimize the overall objective of the imaging pipeline: Extracting clinically useful and actionable information. To achieve this step change, the project aims to develop novel deep learning approaches for image acquisition, reconstruction, analysis and interpretation that can be trained in an end-to-end fashion, allowing fast and more efficient imaging.
Wissenschaftliches Gebiet
Programm/Programme
Thema/Themen
Finanzierungsplan
ERC-ADG - Advanced GrantGastgebende Einrichtung
81675 Muenchen
Deutschland