Abstract
Accurate registration of human lungs in CT images is required for many applications in pulmonary image analysis and used for example for atlas generation. While various registration approaches have been developed in the past, the correct alignment of the interlobular fissures is still challenging for many reasons, especially for inter-patient registration. Fissures are depicted with very low contrast and their proximity in the image shows little detail due to the lack of vessels. Moreover, iterative registration algorithms usually require the objects to be overlapping in both images to find the right transformation, which is often not the case for fissures.
In this work, a novel approach is presented for integrated lobe segmentation and intensity-based registration aiming for a better alignment of the interlobular fissures. To this end, level sets with a shape-based fissure attraction term are used to formulate a new condition in the registration framework. The method is tested for pairwise registration of lung CT scans of nine different subjects and the results show a significantly improved matching of the pulmonary lobes after registration.
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References
Brock, K.K.: Deformable Registration Accuracy Consortium: Results of a multi-institution deformable registration accuracy study (MIDRAS). Int. J. Radiat. Oncol. Biol. Phys. 76(2), 583–596 (2010)
Murphy, K., van Ginneken, B., Reinhardt, J.M., Kabus, S., Ding, K., et al.: Evaluation of Registration Methods on Thoracic CT: The EMPIRE10 Challenge. IEEE Trans. Med. Imag. 30(11), 1901–1920 (2011)
Yezzi, A., Zöllei, L., Kapur, T.: A variational framework for integrating segmentation and registration through active contours. Med. Image Anal. 7(2), 171–185 (2003)
Schmidt-Richberg, A., Handels, H., Ehrhardt, J.: Integrated segmentation and non-linear registration for organ segmentation and motion field estimation in 4D CT data. Methods Inf. Med. 48(4), 344–349 (2009)
van Rikxoort, E.M., Prokop, M., de Hoop, B.J., Viergever, M.A., Pluim, J.P.W., van Ginneken, B.: Automatic Segmentation of Pulmonary Lobes Robust against Incomplete Fissures. IEEE Trans. Med. Imag. 29(6), 1286–1296 (2010)
Schmidt-Richberg, A., Ehrhardt, J., Wilms, M., Werner, R., Handels, H.: Pulmonary Lobe Segmentation with Level Sets. In: Haynor, D.R., Ourselin, S. (eds.) Proc. SPIE, p. 83142V (2012)
Schmidt-Richberg, A., Ehrhardt, J., Werner, R., Handels, H.: Diffeomorphic Diffusion Registration of Lung CT Images. In: van Ginneken, B., Murphy, K., Heimann, T., Pekar, V., Deng, X. (eds.) Medical Image Analysis for the Clinic: A Grand Challenge, MICCAI 2010, pp. 55–62 (2010)
Brox, T., Weickert, J.: Level Set Segmentation with Multiple Regions. IEEE Trans. Image Process. 15(10), 3213–3218 (2006)
van Rikxoort, E.M., van Ginneken, B., Klik, M., Prokop, M.: Supervised Enhancement Filters: Application to Fissure Detection in Chest CT Scans. IEEE Trans. Med. Imag. 27(1), 1–10 (2008)
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Schmidt-Richberg, A., Ehrhardt, J., Werner, R., Handels, H. (2012). Lung Registration with Improved Fissure Alignment by Integration of Pulmonary Lobe Segmentation. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012. MICCAI 2012. Lecture Notes in Computer Science, vol 7511. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33418-4_10
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DOI: https://doi.org/10.1007/978-3-642-33418-4_10
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