EMPIRE10 (Evaluation of Methods for Pulmonary Image REgistration 2010) is a public platform for fair and meaningful comparison of registration algorithms which are applied to a database of intrapatient thoracic CT image pairs. Evaluation of nonrigid registration techniques is a nontrivial task. This is compounded by the fact that researchers typically test only on their own data, which varies widely. For this reason, reliable assessment and comparison of different registration algorithms has been virtually impossible in the past. In this work we present the results of the launch phase of EMPIRE10, which comprised the comprehensive evaluation and comparison of 20 individual algorithms from leading academic and industrial research groups. All algorithms are applied to the same set of 30 thoracic CT pairs. Algorithm settings and parameters are chosen by researchers expert in the configuration of their own method and the evaluation is independent, using the same criteria for all participants. All results are published on the EMPIRE10 website (http://empire10.isi.uu.nl). The challenge remains ongoing and open to new participants. Full results from 24 algorithms have been published at the time of writing. This paper details the organization of the challenge, the data and evaluation methods and the outcome of the initial launch with 20 algorithms. The gain in knowledge and future work are discussed.

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Abstract.We present a new image registration based method for monitoring regional disease progression in longitudinal image studies of lung disease. A free-form image registration technique is used to match a baseline 3D CT lung scan onto a following scan. Areas with lower intensity in the following scan compared with intensities in the deformed baseline image indicate local loss of lung tissue that is associated with progression of emphysema. To account for differences in lung intensity owing to differences in the inspiration level in the two scans rather than disease progression, we propose to adjust the density of lung tissue with respect to local expansion or compression such that the total weight of the lungs is preserved during deformation. Our method provides a good estimation of regional destruction of lung tissue for subjects with a significant difference in inspiration level between CT scans and may result in a more sensitive measure of disease progression than standard quantitative CT measures.

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In this paper we propose a registration method that combines intensity information with geometrical information in the form of curves and surfaces derived from lung CT images. Vessel tree centerlines and lung surfaces were extracted from segmented structures. First, a current-based registration was applied to align the pulmonary vessel tree and the lung surfaces. Subsequently, the resulting deformation field was used to constrain an intensity-based registration method. We applied the combined registration on a set of image pairs, extracted at the end exhale and the end inhale phases of 4D-CT scans. The proposed combined registration was compared to intensity-based registration, using a set of manually selected landmarks. The proposed registration decreases the mean and the standard deviation of the target registration errors for all 5 cases to on average 1.47±1.05 mm, compared to the intensity-based registration without constraint 1.74 ± 1.31 mm.Index Terms-Image registration, BSplines, currents, lung CT. INTRODUCTIONThe ultimate goal of any registration algorithm is to establish dense point-to-point correspondence between two images. Generally, registration of lung CT images is a difficult problem due to the possible large variation between the scans. Scans of the same patient taken at maximum inspiration, can have more than 0.5 liter difference in lung volume. The registration of end exhale and end inhale phases of 4D-CT lung images is an even more difficult problem due to the large and non-uniform deformations during the breathing cycle [1].Image registration methods can be divided into two groups of methods: intensity-based and feature-based. A feature-based method establishes deformations based on lowdimensional features, derived from the original images, while * This work is financially supported by the Danish Council for Strategic Research under the Programme Commission for Nanoscience and Technology, Biotechnology and IT (NABIIT), the Netherlands Organization for Scientific Research (NWO), and AstraZeneca, Lund, Sweden. Authors would like to thank Jon Sporring, Copenhagen University, Department of Computer Science, eScience Center, for fruitful discussions.intensity-based method considers intensity information over complete image. The state-of-the art registration methods for lung CT images are mainly intensity-based approaches [2] because the feature-based methods generally produce less accurate results [3].Recently, Li et al. [4] developed an image registration algorithm where the intensity-based registration was improved with the subsequent bio-mechanical simulation of lung inflation. Results showed an improvement in both accuracy of registration and physical plausibility of the deformation field for the combined approach. We previously developed a feature-based algorithm for registering lung CT images and compared it to intensity-based registration [5]. The overall accuracy of the feature-based algorithm was slightly worse than that of the intensity-based algorithm, but in 35 % of landmarks t...