Segmentation of 3D Tubular Tree Structures in Medical Images ...

Abstract
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
of tubular objects using the Gradient Vector Flow to address limitations of the typically
used Gaussian scale space. We propose two methods for grouping and linkage of sets of
unconnected tubular structures into tubular tree structures. One enables an extraction of
high quality centerlines in regions that deviate significantly from a typical tubular shape,
while the other one allows for a separation of interwoven tubular tree structures as well
as handling of various kinds of disturbances. To accurately segment the identified tubular
structures two methods are developed. One solves the segmentation task in a globally
optimal way using graph cuts, while the other one segments according to the edge closest
to the centerline.
Based on these methods, different applications for segmentation of blood vessel trees
(liver vasculature and coronary arteries) and airway trees in CT datasets are developed.
The methods are evaluated on clinical datasets and compared to results achieved with other
state-of-the-art methods developed for the same task. The results successfully demonstrate
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