Matching 3-D prone and supine CT colonography scans using graphs

Graph matching survey for medical imaging: On the way to deep learning

The interest on graph matching has not stopped growing since the late seventies. The basic idea of graph matching consists of generating graph representations of different data or structures and compare those representations by searching correspondences between them. There are manifold techniques that have been developed to find those correspondences and the choice of one or another depends on the characteristics of the application of interest. These applications range from pattern recognition (e.g. biometric identification) to signal processing or artificial intelligence. One of the aspects that make graph matching so attractive is its ability to facilitate data analysis, and medical imaging is one of the fields that can benefit from this in a greater extent. The potential of graph matching to find similarities and differences between data acquired at different points in time shows its potential to improve diagnosis, follow-up of human diseases or any other of the clinical scenarios that require comparison between different datasets. In spite of the large amount of papers that were published in this field to the date there is no survey paper of graph matching for clinical applications. This survey aims to fill this gap.

Feasibility of using the marginal blood vessels as reference landmarks for CT colonography

OBJECTIVE

The purpose of this study was to show the spatial relationship of the colonic marginal blood vessels and the teniae coli on CT colonography (CTC) and the use of the marginal blood vessels for supine-prone registration of polyps and for determination of proper connectivity of collapsed colonic segments.

MATERIALS AND METHODS

We manually labeled the marginal blood vessels on 15 CTC examinations. Colon segmentation, centerline extraction, teniae detection, and teniae identification were automatically performed. For assessment of their spatial relationships, the distances from the marginal blood vessels to the three teniae coli and to the colon were measured. Student t tests (paired, two-tailed) were performed to evaluate the differences among these distances. To evaluate the reliability of the marginal vessels as reference points for polyp correlation, we analyzed 20 polyps from 20 additional patients who underwent supine and prone CTC. The average difference of the circumferential polyp position on the supine and prone scans was computed. Student t tests (paired, two-tailed) were performed to evaluate the supine-prone differences of the distance. We performed a study on 10 CTC studies from 10 patients with collapsed colonic segments by manually tracing the marginal blood vessels near the collapsed regions to resolve the ambiguity of the colon path.

RESULTS

The average distances (± SD) from the marginal blood vessels to the tenia mesocolica, tenia omentalis, and tenia libera were 20.1 ± 3.1 mm (95% CI, 18.5-21.6 mm), 39.5 ± 4.8 mm (37.1-42.0 mm), and 36.9 ± 4.2 mm (34.8-39.1 mm), respectively. Pairwise comparison showed that these distances to the tenia libera and tenia omentalis were significantly different from the distance to the tenia mesocolica (p < 0.001). The average distance from the marginal blood vessels to the colon wall was 15.3 ± 2.0 mm (14.2-16.3 mm). For polyp localization, the average difference of the circumferential polyp position on the supine and prone scans was 9.6 ± 9.4 mm (5.5-13.7 mm) (p = 0.15) and expressed as a percentage of the colon circumference was 3.1% ± 2.0% (2.3-4.0%) (p = 0.83). We were able to trace the marginal blood vessels for 10 collapsed colonic segments and determine the paths of the colon in these regions.

CONCLUSION

The marginal blood vessels run parallel to the colon in proximity to the tenia mesocolica and enable accurate supine-prone registration of polyps and localization of the colon path in areas of collapse. Thus, the marginal blood vessels may be used as reference landmarks complementary to the colon centerline and teniae coli.

Endoluminal surface registration for CT colonography using haustral fold matching

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Novel haustral fold matching algorithm.

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Achieves 96.1% mean accuracy over 1743 reference points in 17 CTC datasets.

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New initialisation to non-rigid intensity-based surface registration method.

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Full method shows 6.0 mm mean error.

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Use of initialisation shows significant improvement (p < 0.001).

Computed Tomographic (CT) colonography is a technique used for the detection of bowel cancer or potentially precancerous polyps. The procedure is performed routinely with the patient both prone and supine to differentiate fixed colonic pathology from mobile faecal residue. Matching corresponding locations is difficult and time consuming for radiologists due to colonic deformations that occur during patient repositioning.

We propose a novel method to establish correspondence between the two acquisitions automatically. The problem is first simplified by detecting haustral folds using a graph cut method applied to a curvature-based metric applied to a surface mesh generated from segmentation of the colonic lumen. A virtual camera is used to create a set of images that provide a metric for matching pairs of folds between the prone and supine acquisitions. Image patches are generated at the fold positions using depth map renderings of the endoluminal surface and optimised by performing a virtual camera registration over a restricted set of degrees of freedom. The intensity difference between image pairs, along with additional neighbourhood information to enforce geometric constraints over a 2D parameterisation of the 3D space, are used as unary and pair-wise costs respectively, and included in a Markov Random Field (MRF) model to estimate the maximum a posteriori fold labelling assignment.

The method achieved fold matching accuracy of 96.0% and 96.1% in patient cases with and without local colonic collapse. Moreover, it improved upon an existing surface-based registration algorithm by providing an initialisation. The set of landmark correspondences is used to non-rigidly transform a 2D source image derived from a conformal mapping process on the 3D endoluminal surface mesh. This achieves full surface correspondence between prone and supine views and can be further refined with an intensity based registration showing a statistically significant improvement (p < 0.001), and decreasing mean error from 11.9 mm to 6.0 mm measured at 1743 reference points from 17 CTC datasets.