Descrizione del progetto
Apprendimento profondo per migliorare la diagnostica per immagini
La diagnostica per immagini ha rivoluzionato la diagnosi, il trattamento e il follow-up, fornendo informazioni fondamentali sull’anatomia e la fisiologia con una risoluzione spaziale molto elevata. Tuttavia, il processo di immaginografia può essere stressante per i pazienti, ed è difficile in presenza di movimento. L’obiettivo chiave del progetto Deep4MI, finanziato dall’UE, è di far progredire e automatizzare la diagnostica per immagini in modo da fornire una maggiore accuratezza diagnostica e prognostica per il processo decisionale clinico. Utilizzando tecniche di apprendimento automatico e profondo, gli scienziati miglioreranno l’acquisizione, la ricostruzione e l’analisi delle immagini per estrarre più informazioni cliniche dalle immagini mediche e ottimizzare l’interpretazione dei risultati.
Obiettivo
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.
Campo scientifico
Programma(i)
Argomento(i)
Meccanismo di finanziamento
ERC-ADG - Advanced GrantIstituzione ospitante
81675 Muenchen
Germania