Auflistung nach Autor:in "Andresen, Julia"

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Analyzing the progression of pathologies in medical images
(2025) Andresen, Julia
Medical image analysis is a key component of modern healthcare, required not only for diagnosis, but also for treatment planning and disease monitoring. The number of medical images acquired every day is constantly increasing and with it the need for automated tools to process, segment and interpret these images efficiently and reliably. Over the past decade, deep learning-based approaches, especially convolutional neural networks, have revolutionized the field providing unprecedented performances for almost all medical image analysis tasks, including semantic segmentation and image registration. However, the training of deep neural networks needs vast amounts of data, whereas most annotated medical datasets are small. The manual delineation of anatomical and pathological structures needs expert knowledge, and is both time-consuming and error-prone. These problems are even more severe in the analysis of disease progression, where not just one image but several have to be analyzed together. Furthermore, pathologies exhibit higher variability than anatomical structures and occupy comparatively small image areas, further increasing the data demands for training. This dissertation aims to develop deep learning-based algorithms for the automatic analysis of medical time series image data, focusing on pathological progression over time, such as retinal fluid in optical coherence tomography and brain lesions in magnetic resonance imaging. The main goal is to segment pathologies across all time points in order to monitor disease progression. Expert segmentations are typically unavailable for extensive time series data, requiring weakly supervised or fully unsupervised methods. Therefore, longitudinal registration of medical images is investigated as a tool for pathology tracking and unsupervised segmentation. To achieve the goals described, the present work follows three complementary research directions. First, unsupervised clustering is used to segment individual images. Second, registration-based approaches are developed for the joint analysis of longitudinal data with simultaneous segmentation of non-correspondences that reflect evolving or disappearing pathologies. Third, registration approaches inspired by metamorphosis models are used to model the formation of new pathologies. To improve the plausibility of the resulting deformations, these models are designed to separate displacements of anatomical structures from volumetric changes of the pathologies. The methods presented in this thesis enable the unsupervised segmentation of pathological structures, without relying on manually generated pathology segmentations. By leveraging weak supervision through anatomical labels and exploiting temporal information in longitudinal data, the proposed approaches can identify disease-related changes in an unsupervised manner. Overall, this work provides novel, annotation-efficient strategies for the automated analysis of medical image time series data, with the potential to support clinical workflows in the assessment of disease progression.