Semi-automatic construction of reference standards for evaluation of image registration

Abstract

Quantitative evaluation of image registration algorithms is a difficult and under-addressed issue due to the lack of a reference standard in most registration problems. In this work a method is presented whereby detailed reference standard data may be constructed in an efficient semi-automatic fashion. A well-distributed set of n landmarks is detected fully automatically in one scan of a pair to be registered. Using a custom-designed interface, observers define corresponding anatomic locations in the second scan for a specified subset of s of these landmarks. The remaining n-s landmarks are matched fully automatically by a thin-plate-spline based system using the s manual landmark correspondences to model the relationship between the scans. The method is applied to 47 pairs of temporal thoracic CT scans, three pairs of brain MR scans and five thoracic CT datasets with synthetic deformations. Interobserver differences are used to demonstrate the accuracy of the matched points. The utility of the reference standard data as a tool in evaluating registration is shown by the comparison of six sets of registration results on the 47 pairs of thoracic CT data.

Analysis of Long-Term 4-Dimensional Computed Tomography Regional Ventilation After Radiation Therapy

“…First, we identified approximately 100 corresponding landmarks (vessel bifurcations) between the 2 image sets using the Ix software (20). Then we created a deformation field from these landmarks using a spline kernel transform.…”

Accuracy Estimation for Medical Image Registration Using Regression Forests

“…Patients are within the range of 49 to 78 years old and for each patient a baseline image and a follow-up image (after 30 months) are available in which 100 well-distributed corresponding landmarks are selected semi-automatically on distinctive locations [11]. The residual Euclidean distance after registration between the corresponding points can be seen as the accuracy of the registration.…”

Deformable registration of the inflated and deflated lung in cone‐beam CT‐guided thoracic surgery: Initial investigation of a combined model‐ and image‐driven approach

“…Such work will benefit from semiautomatic target identification methods to further streamline error analysis and accuracy validation. 56 The additional trials will be enabled in part by integration of the model-driven and image-driven methods in an in-house image guidance system, 11 also facilitating workflow analysis and preclinical testing in CBCT guidance of thoracic surgery. Previous work in the context of spine and skull base surgery have demonstrated the utility of such application-specific modules for early translational research in seeing CBCT guidance from the laboratory to early clinical studies.…”