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

Abstract

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.

Fig. 1

A schematic description of the artery/vein separation problem and its solution using a multi-scale topo-morphologic opening algorithm. In (a), hollow dots indicate morphological erosion while the green disks represent multi-scale morphological operators.

Fig. 2

An integrated 2D and 3D graphical user interface system for A/V separation. The left window illustrates the 2D interface for editing seeds/separators with several features including various overlay options. The window on the right presents 3D rendering of vasculature along with color-coded display of current A/V separation with the cursors in 2D and 3D windows being inter-connected.

Fig. 3

Illustration of the steps during local update. (a) Vessel structures without A/V separation; one seed is selected for each of the A/V trees. (b) Results of initial A/V separation; the location with a failure in A/V separation is indicated. (c) Results of local update after adding a new seed. Inherited seeds are indicated with black circles. (d) Results of global update.

Fig. 4

Illustration of user interaction steps within the A/V separation GUI system. (a) An axial slice view with a visually detected mistake in A/V separation as indicated by an arrow. (b–c) Normal and zoomed in 3D rendering of the A/V separation results with arrows indicating to location of the mistake in A/V separation. (d–f) Same as (a–c) after correcting the mistake using an additional user specified seed.

Fig. 5

3D rendering of five computer generated phantoms with bifurcation used in our experiments.

Fig. 6

Results of applying the MSTMO algorithm to computer-generated phantoms. (a–f) Results of phantoms with 5% overlap at 3 × 3 × 3 (a, b), 4 × 4 × 4 (c, d) and 5 × 5 × 5 (e, f) down sampling. (a,b) 3D rendering of the phantom images before (a) and after (b) applying MSTMO algorithm. On each image, several 2D cross sectional images are presented to illustrate relative overlap at various scales. (g–l), (m–r), (s–x) Same as (a–f) but for 10%, 15%, and 25% overlaps. At 25% overlap and 4 × 4 × 4 and 5 × 5 × 5 downsampling rate, the method has failed to sucessfully separate two objects at small scales.

Fig. 7

Results of applying the MSTMO algorithm to three computer-generated phantom images. (a)–(f) Results of phantoms with increasing overlap and downsampling rates – (a, b) 10% overlap and 3 × 3 × 3 downsampling; (c, d) 15% overlap and 4 × 4 × 4 downsampling; (e, f) 25% overlap and 5 × 5 × 5 down sampling. (g–l) Same as (a–f) but for another base phantom.

Fig. 8

A/V separation results on a pulmonary pig vessel cast phantom. (a) A photograph of the phantom. (b) A coronal slice from the original CT image of the phantom data with different contrast for A/V trees. (c) Same as (b) after contrast elimination. (d) True A/V separation from the original contrast-separated CT data. (e) 3D rendering of the contrast-eliminated vasculature. (f) A/V separation from (e) using the MSTMO algorithm.

Fig. 9

Results of A/V separation on a pulmonary pig vessel cast phantoms. (a) A coronal image slice from the CT data of pig lung vessel cast phantom at 0.6mm slice thickness. (b) 3D rendering of vasculature after elimination of contrast separation. (c) Results of A/V separation. (d–f) Same as (a–c) but at 1.5mm slice thickness.

Fig. 10

Results of A/V separation on contrast-enhanced in vivo CT images of a pig’s lung at three different PEEPs. (a,d,g) Visually matched coronal slices from original pulmonary CT images at 7.5cm (a), 12cm (d) and 18cm (g) H₂O PEEPs. (b,e,h) 3D rendering of the segmented vasculature from two CT data sets of (a,d,g). (c,f,i) 3D rendering of A/V separation results using the MSTMO algorithm.

Fig. 11

Results of A/V separation on pulmonary CT data. (a) A coronal image slice. (b) 3D rendering of the vasculature. (c,d,e) Color-coded rendering of A/V separations using seeds from three independent experts.