Anatomy-Aided Deep Learning for Medical Image Segmentation with Application to Epicardial Adipose Tissue
Lu Liu is a PhD student in the Department Datamanagement & Biometrics. Promotors are prof.dr.ir. R.N.J. Veldhuis and prof.dr. C. Brune from the Faculty of Electrical Engineering, Mathematics and Computer Science and prof.dr.ir. P.M.A. van Ooijen from University Medical Center Groningen.
Medical image segmentation plays a pivotal role in modern healthcare by enabling precise diagnosis, treatment planning, and continuous monitoring of diseases. Among various segmentation tasks, the segmentation of epicardial adipose tissue (EAT) in low-dose computed tomography (LDCT) scans has gained attention due to its relevance in cardiovascular health. EAT is a vital fat depot surrounding the heart, closely associated with inflammatory mediators and cytokines, making it a crucial imaging biomarker for various cardiovascular conditions. LDCT is particularly suited for EAT segmentation, offering detailed anatomical information with minimal radiation exposure, thus allowing for repeated scans and large-scale studies. These studies are essential for assessing cardiovascular risk factors, including EAT volume, and identifying multiple biomarkers in a single scan for comprehensive cardiovascular health evaluations.
However, segmenting EAT in LDCT scans presents significant challenges. The irregular shape and non-uniform distribution of EAT around the heart make it difficult to distinguish from surrounding tissues. Additionally, LDCT imaging is prone to noise and artifacts, which obscure image clarity and complicate the accurate delineation of EAT boundaries. The lack of specialized annotated datasets further hampers the development of reliable segmentation algorithms. Although advanced deep learning approaches have been applied to EAT segmentation, they often require large annotated datasets, are sensitive to noise, and struggle with interpreting features without explicit anatomical priors. To address these challenges, this thesis emphasizes the integration of anatomical information, particularly the shape of the pericardium, into segmentation methodologies. This approach aims to improve the accuracy, efficiency, and clinical relevance of EAT segmentation, ultimately contributing to better patient care and more informed medical decision-making.
The thesis is built on a foundation of thorough literature review, examining existing anatomy-aided deep learning techniques for medical image segmentation. A significant contribution of the research is the creation of a specialized dataset consisting of 154 LDCT scans, annotated with pericardium and pixel-wise EAT labels. This dataset serves as a critical resource for training and evaluating segmentation algorithms. The thesis explores various deep learning models, such as 3D U-net and its variants, and evaluates their performance using traditional metrics like the Dice coefficient and statistical analyses like Pearson correlation and Bland-Altman analysis. A novel shape-based loss function derived from Fourier descriptors is introduced to enhance segmentation accuracy, particularly in challenging scenarios. Additionally, the thesis explores the use of Fourier neural operators to generalize shape encoding in the Fourier space, addressing the limitations of traditional methods and improving the generalization capabilities of models across different medical image segmentation tasks. Through these advancements, the thesis aims to provide state-of-the-art tools for healthcare professionals, driving progress in medical image segmentation and contributing to enhanced clinical outcomes.
