Jennifer Alvén, Electrical Engineering

Title: Combining Shape and Learning for Medical Image Analysis-Robust, Scalable and Generalizable Registration and Segmentation

​Jennifer Alvén is a PhD student at the Division of Signal processing and Biomedical Engineering
Faculty opponent is Associate Professor Marleen de Bruijne, Erasmus University Rotterdam, The Netherlands
Examiner is Professor Fredrik Kahl, Division of Signal processing and Biomedical Engineering
Automatic methods with the ability to make accurate, fast and robust assessments of medical images are highly requested in medical research and clinical care. Excellent automatic algorithms are characterized by speed, allowing for scalability, and an accuracy comparable to an expert radiologist. They should produce morphologically and physiologically plausible results while generalizing well to unseen and rare anatomies. Still, there are few, if any, applications where today's automatic methods succeed to meet these requirements.

 The focus of this thesis is two tasks essential for enabling automatic medical image assessment, medical image segmentation and medical image registration. Medical image registration, i.e. aligning two separate medical images, is used as an important sub-routine in many image analysis tools as well as in image fusion, disease progress tracking and population statistics. Medical image segmentation, i.e. delineating anatomically or physiologically meaningful boundaries, is used for both diagnostic and visualization purposes in a wide range of applications, e.g. in computer-aided diagnosis and surgery.

 The thesis comprises five papers addressing medical image registration and/or segmentation for a diverse set of applications and modalities, i.e. pericardium segmentation in cardiac CTA, brain region parcellation in MRI, multi-organ segmentation in CT, heart ventricle segmentation in cardiac ultrasound and tau PET registration. The five papers propose competitive registration and segmentation methods enabled by machine learning techniques, e.g. random decision forests and convolutional neural networks, as well as by shape modelling, e.g. multi-atlas segmentation and conditional random fields.
Keywords: medical image segmentation, medical image registration, machine learning, shape models, multi-atlas segmentation, feature-based registration, convolutional neural networks, random decision forests, conditional random fields

Category Thesis defence
Location: EA, lecture hall, Hörsalsvägen 11, EDIT trappa C, D och H
Starts: 07 February, 2020, 10:00
Ends: 07 February, 2020, 13:00

Published: Fri 10 Jan 2020.