Deep Learning for Extracting Tree Structures in Medical Images
Research Project , 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.

Participants

Fredrik Kahl (contact)

Professor vid Chalmers, Electrical Engineering, Signalbehandling och medicinsk teknik, Imaging and Image Analysis

Jennifer Alvén

Doktorand vid Chalmers, Electrical Engineering, Signalbehandling och medicinsk teknik, Imaging and Image Analysis

Collaborations

Sahlgrenska University Hospital

Gothenburg, Sweden

Funding

Chalmers AI Research Centre (CHAIR)

Funding Chalmers participation during 2020–2021

Related Areas of Advance and Infrastructure

Information and Communication Technology

Areas of Advance

More information

Latest update

2020-02-04