Deep Learning för att extrahera trädstrukturer i medicinska bilder
Forskningsprojekt , 2020 – 2021

Machine learning methods have become an indispensable tool for classification problems in medical image analysis. However, problems involving structured outputs, such as automatically extracting the coronary artery tree in a CT image, remain challenging and learning methods used for classification cannot directly be applied. In this project, we will develop methods and theory for robustly computing structured outputs based on deep learning in combination with shape modelling.

 

The project is in collaboration with the SCAPIS study, a population study with collected CT examinations in over 28,000 individuals. These examinations can be used to analyze possible atherosclerosis in the coronary arteries of the heart. The size and appearance of atherosclerosis are important biomarkers and good predictors for determining the risk of getting a myocardial infarction in the future. Training data from the SCAPIS study will consist of over 600 manually annotated cases.

Deltagare

Fredrik Kahl (kontakt)

Professor vid Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik, Digitala bildsystem och bildanalys

Jennifer Alvén

Doktor vid Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik, Digitala bildsystem och bildanalys

Samarbetspartners

Sahlgrenska universitetssjukhuset

Gothenburg, Sweden

Finansiering

Chalmers AI-forskningscentrum (CHAIR)

Finansierar Chalmers deltagande under 2020–2021

Relaterade styrkeområden och infrastruktur

Informations- och kommunikationsteknik

Styrkeområden

Mer information

Senast uppdaterat

2020-02-04