Segmentation Workflow

From 2006 until 2012, our team has developed a semi-automated segmentation software that addresses the vast majority of segmentation needs discussed above.

The main steps of our method are illustrated in Figure 3.4 and discussed below.

Figure 3.4: Overview of software interaction.

User tasks, software tasks and graphic display are illustrated in the left, middle and right columns, respectively (36).

504 C. Kauffmann et al. / European Journal of Radiology 77 (2011) 502–508

Fig. 1. Overview of software interaction. User tasks, software tasks and graphic display are illustrated in the left, middle and right column, respectively.

2.6.3. Validity

For the validity analysis, linear regression and Bland–Altman analysis were used to assess agreement between the two (software and manual DO) methods of measurement. Linear regression anal- ysis was performed separately for measurement taken on baseline

and follow-up examinations. Means of the two readings (sessions 1 and 2) were calculated for the software and for each of the two radiologists (1 and 3). The 95% CI for the slope and intercept are reported. If the slope of the line is close to unity and the intercept close to zero, this implies that the two methods of measuring D-max

Fig. 2. Abdominal aortic aneurysm in a 79-year-old female. Left picture shows 3D volume rendering displays with 3D AAA model overlay. Different diameter values are color-coded, the smallest diameters are represented in blue and the largest in red. The automatically calculated D-max is displayed in the right picture by the red line.

After loading the CT dataset in DICOM format, the interactive method consists of the following steps:

1) The user clicks on two anatomical landmarks: an entry point on the supra-renal aortic lumen at the level of the celiac trunk and an exit point on one of the common iliac arteries. It is preferable to select the iliac artery that is best aligned with the aortic centerline to prevent distorsions of the stretched longitudinal view of the path-based image.

2) The software will then compute the optimal lumen path and automatically segment the 3D lumen using an hybrid central processing unit (CPU)-graphics processing unit (GPU) implementation of the Dijkstra’s and Bellman-Ford’s shortest paths algorithm (31). Alternatively, if the CT study is unenhanced, it is possible to create a manual path by creating a centerline from several clicks (approximately seven) from the celiac trunk to one of the iliac arteries.

3) The software will compute a smooth luminal path and GPU-based image reformation. These images are radial reformations passing through the lumen centerline straightened in the middle of the image. If this path has reconstruction artifacts, it can be corrected manually. Otherwise, the user proceeds to the next step.

4) The user performs semi-automated aneurysm wall segmentation on 4 to 8 radial image reformations along the path axis with an active contour process. With this reconstruction plane, it is easier to delineate the AAA wall even when the mural thrombus and adjacent soft tissues have similar densities (Hounsfield units).

5) A key feature is that the software interpolates the aortic contours between the radial image reformations, which dramatically shortens the segmentation time. The user may interactively validate the contour and perform editing, but only if needed (Figure 3.5).

Figure 3.5: Radial and longitudinal stretch views for segmentation.

(A) Radial image reformation. This image is perpendicular to the centerline and displays 8

radial lines. (B) Longitudinal stretched view. This image corresponds to the radial plane

highlighted in red in the first image. The user corrects the segmentation by modifying the outer wall contour (green line). Interactive navigation between radial planes facilitates delineation of the outer wall, even when the thrombus density is remarkably similar to the adjacent soft tissues.

6) Once the user approves the segmentation, the software computes a centerline based on the outer wall of the AAA and displays a 3D mathematical model of the AAA with distinct thrombus and lumen reconstructions.

7) Finally, the software performs automated calculation of the Dmax perpendicular to the new central line was processed (Figure 3.6). Thrombus, lumen, and whole AAA volumes are also computed. The work session can be saved at any time in extensible markup language (XML) format.

The success of this interactive or supervised method lies in the complementarity between user and software tasks. The details of the mathematical method are provided in the following reference by Kauffmann et al. (31). A patent was filed for this method on June 11, 2008 and issued on Oct 18, 2011 for this invention (103).

CHAPTER 3.SEGMENTATION SOFTWARE

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Figure 3.6: AAA model in a 72-year-old man with a Dmax of 6.97 cm.

(A) AAA surface model with optimized path (yellow line), lumen (red) and outer-wall mesh

(green) of the AAA. (B) Dmax values are color-coded at each location: the smallest

diameters are represented in blue and the largest in red. (C) The automatically calculated

Dmax is highlighted by the blue dashed line (30).

Optimization

To shorten the computing time, some tasks were implemented and executed on the GPU. This hybrid CPU-GPU approach leverages the rapid increase in GPU programmability and capability in recent years. Computationally demanding, complex problems are mapped to the GPU. In our implementation, two algorithms are GPU-based: the automatic lumen segmentation and the curved image reformation. In a recent publication by our team, the average segmentation time was 3.0 min ± 1.1 min per case (30).

3D modeling

The 3D mathematical model (mesh) generated by this method can be exported in VRML format and read by a modeling software or interactive programming environment Please cite this article in press as: Kauffmann C, et al. Measurements and detection of abdominal aortic aneurysm growth: Accuracy and reproducibility of a segmentation software. Eur J Radiol (2011), doi:10.1016/j.ejrad.2011.04.044

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