A new paradigm of interactive artery/vein separation in noncontrast pulmonary CT imaging using multiscale topomorphologic opening.

Distinguishing pulmonary arterial and venous (A/V) trees via in vivo imaging is a critical first step in the quantification of vascular geometry for the purpose of diagnosing several pulmonary diseases and to develop new image-based phenotypes. A multiscale topomorphologic opening (MSTMO) algorithm has recently been developed in our laboratory for separating A/V trees via noncontrast pulmonary human CT imaging. The method starts with two sets of seeds-one for each of A/V trees and combines fuzzy distance transform and fuzzy connectivity in conjunction with several morphological operations leading to locally adaptive iterative multiscale opening of two mutually conjoined structures. In this paper, we introduce the methods for handling "local update" and "separators" into our previous theoretical formulation and incorporate the algorithm into an effective graphical user interface (GUI). Results of a comprehensive evaluative study assessing both accuracy and reproducibility of the method under the new setup are presented and also, the effectiveness of the GUI-based system toward improving A/V separation results is examined. Accuracy of the method has been evaluated using mathematical phantoms, CT images of contrast-separated pulmonary A/V casting of a pig's lung and noncontrast pulmonary human CT imaging. The method has achieved 99% true A/V labeling in the cast phantom and, almost, 92-94% true labeling in human lung data. Reproducibility of the method has been evaluated using multiuser A/V separation in human CT data along with contrast-enhanced CT images of a pig's lung at different positive end-expiratory pressures (PEEPs). The method has achieved, almost, 92-98% agreements in multiuser A/V labeling with ICC for A/V measures being over 0.96-0.99. Effectiveness of the GUI-based method has been evaluated on human data in terms of improvements of accuracy of A/V separation results and results have shown 8-22% improvements in true A/V labeling. Both qualitative and quantitative results found are very promising.