Segmentation of 3D tubular tree structures in medical images
Christian H. Bauer · 2022
The segmentation of tubular tree structures like vessel systems in volumetric medical images is of vital interest for many medical applications. However, a diverse set of challenging objectives and problems is related to this task in different application domains. In this work, we develop and evaluate methods to address these issues. To accomplish the segmentation of heavily branched structures in a robust manner, we propose a generally applicable three-step approach consisting of: (i) a bottom-up identification of tubular structures followed by (ii) a grouping and linkage of these tubular structures into tree structures that are (iii) used as a prior for the actual segmentation. This approach incorporates additional prior knowledge compared to conventional approaches: the individual tubular structures have to be connected with each other and – from a biological perspective – to be supplied. In this way, we achieve a high robustness regarding the structural correctness of the segmentation results. We develop and investigate novel methods for each of these processing steps addressing the needs of different applications. In particular, we present a novel approach for detection