Abstract. This paper describes a framework for evaluating airway extraction algorithms in a standardized manner and establishing reference segmentations that can be used for future algorithm development. Because of the sheer difficulty of constructing a complete reference standard manually, we propose to construct a reference using results from the algorithms being compared, by splitting each airway tree segmentation result into individual branch segments that are subsequently visually inspected by trained observers. Using the so constructed reference, a total of seven performance measures covering different aspects of segmentation quality are computed. We evaluated 15 airway tree extraction algorithms from different research groups on a diverse set of 20 chest CT scans from subjects ranging from healthy volunteers to patients with severe lung disease, who were scanned at different sites, with several different CT scanner models, and using a variety of scanning protocols and reconstruction parameters.

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This chapter presents a comparative study of texture classification in computed tomography images of the human lungs. Popular texture descriptors used in the medical image analysis literature for texture-based emphysema classification are described and evaluated within the same classification framework. Further, it is investigated whether combining the different descriptors is beneficial.

Radiomics is to provide quantitative descriptors of normal and abnormal tissues during classification and prediction tasks in radiology and oncology. Quantitative Imaging Network members are developing radiomic “feature” sets to characterize tumors, in general, the size, shape, texture, intensity, margin, and other aspects of the imaging features of nodules and lesions. Efforts are ongoing for developing an ontology to describe radiomic features for lung nodules, with the main classes consisting of size, local and global shape descriptors, margin, intensity, and texture-based features, which are based on wavelets, Laplacian of Gaussians, Law’s features, gray-level co-occurrence matrices, and run-length features. The purpose of this study is to investigate the sensitivity of quantitative descriptors of pulmonary nodules to segmentations and to illustrate comparisons across different feature types and features computed by different implementations of feature extraction algorithms. We calculated the concordance correlation coefficients of the features as a measure of their stability with the underlying segmentation; 68% of the 830 features in this study had a concordance CC of ≥0.75. Pairwise correlation coefficients between pairs of features were used to uncover associations between features, particularly as measured by different participants. A graphical model approach was used to enumerate the number of uncorrelated feature groups at given thresholds of correlation. At a threshold of 0.75 and 0.95, there were 75 and 246 subgroups, respectively, providing a measure for the features’ redundancy.

To develop statistical methods for shapes with a tree-structure, we construct a shape space framework for tree-shapes and study metrics on the shape space. This shape space has singularities which correspond to topological transitions in the represented trees. We study two closely related metrics on the shape space, TED and QED. QED is a quotient euclidean distance arising naturally from the shape space formulation, while TED is the classical tree edit distance. Using Gromov's metric geometry, we gain new insight into the geometries defined by TED and QED. We show that the new metric QED has nice geometric properties that are needed for statistical analysis: Geodesics always exist and are generically locally unique. Following this, we can also show the existence and generic local uniqueness of average trees for QED. TED, while having some algorithmic advantages, does not share these advantages. Along with the theoretical framework we provide experimental proof-of-concept results on synthetic data trees as well as small airway trees from pulmonary CT scans. This way, we illustrate that our framework has promising theoretical and qualitative properties necessary to build a theory of statistical tree-shape analysis.