Abstract
We propose a method to automatically select a treatment plan for radiotherapy of cervical cancer using a Plan-of-the-Day procedure, in which multiple treatment plans are constructed prior to treatment. The method comprises a multi-atlas based segmentation algorithm that uses the selected treatment plan to choose between two atlas sets. This segmentation only requires two registration procedures and can therefore be used in clinical practice without using excessive computation time. Our method is validated on a dataset of 224 treatment fractions for 10 patients. In 37 cases (16%), no recommendation was made by the algorithm due to poor image quality or registration results. In 93% of the remaining cases a correct recommendation for a treatment plan was given.
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Aljabar, P., Heckemann, R.A., Hammers, A., Hajnal, J.V., Rueckert, D.: Multi-atlas based segmentation of brain images: atlas selection and its effect on accuracy. Neuroimage 46, 726–738 (2009)
Asman, A.J., Landman, B.A.: Formulating Spatially Varying Performance in the Statistical Fusion Framework. IEEE Trans. Med. Imaging 31(6), 1326–1336 (2012)
Bondar, M.L., Hoogeman, M.S., Mens, J.W., Quint, S., Ahmad, R., Dhawtal, G., Heijmen, B.J.: Individualized nonadaptive and online-adaptive IMRT treatment strategies for cervical cancer patients based on pre-treatment acquired variable bladder filling CT-scans. Int. J. Radiat. Oncol. Biol. Phys. 83(5), 1617–1623 (2012)
Commowick, O., Akhondi-Asl, A., Warfield, S.K.: Estimating a reference standard segmentation with spatially varying performance parameter: local MAP STAPLE. IEEE Trans. Med. Imaging 31(8), 1593–1606 (2012)
Gill, S., Pham, D., Dang, K., Bressel, M., Kron, T., Siva, S., Tran, P.K., Tai, K.H., Foroudi, F.: Plan of the day selection for online image-guided adaptive post-prostatectomy radiotherapy. Rad. Onco. 107(2), 165–170 (2013)
Heckemann, R.A., Hajnal, J.V., Aljabar, P., Rueckert, D., Hammers, A.: Automatic anatomical brain MRI segmentation combining label propagation and decision fusion. Neuroimage 33, 115–126 (2006)
Langerak, T.R., van der Heide, U.A., Kotte, A.N.T.J., van Vulpen, M., Viergever, M., Pluim, J.P.W.: Label fusion in atlas-based segmentation using a selective and iterative method for performance level estimation (SIMPLE). IEEE Transactions on Medical Imaging 29(12), 2000–2008 (2010)
Klein, S., van der Heide, U.A., Lips, I.M., van Vulpen, M., Staring, M., Pluim, J.P.W.: Automatic segmentation of the prostate in 3D MR images by atlas matching using localized mutual information. Medical Physics 35(4), 1407–1417 (2008)
Langerak, T.R., Berendsen, F.F., van der Heide, U.A., Kotte, A.N.T.J., Pluim, J.P.W.: Multi-atlas-based segmentation with preregistration atlas selection. Med. Phys. 40(9), 091701 (2013)
Murthy, V., Master, Z., Adurkar, P., Mallick, I., Mahantshetty, U., Bakshi, G., Tongaonkar, H., Shrivastava, S.: ’Plan of the day’ adaptive radiotherapy for bladder cancer using helical tomotherapy. Radiother. Oncol. 99(1), 55–60 (2011)
Rohlfing, T., Brandt, R., Menzel, R., Russakoff, D.B., Maurer Jr., C.R.: Quo vadis, atlas-based segmentation? In: Suri, J.S., Wilson, D.L., Laxminarayan, S. (eds.) Handbook of Biomedical Image Analysis. Topics in Biomedical Engineering International Book Series, pp. 435–486 (2005)
Warfield, S.K., Zou, K.H., Wells, W.M.: Simultaneous truth and performance level estimation (STAPLE): an algorithm for the validation of image segmentation. IEEE Transactions on Medical Imaging 23(7), 903–992 (2004)
Wu, G., Wang, Q., Zhang, D., Nie, F., Huang, H., Shen, D.: A generative probability model of joint label fusion for multi-atlas based brain segmentation. Med. Image Anal. (in press)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Langerak, T., Heijkoop, S., Quint, S., Mens, JW., Heijmen, B., Hoogeman, M. (2014). Towards Automatic Plan Selection for Radiotherapy of Cervical Cancer by Fast Automatic Segmentation of Cone Beam CT Scans. In: Golland, P., Hata, N., Barillot, C., Hornegger, J., Howe, R. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014. MICCAI 2014. Lecture Notes in Computer Science, vol 8673. Springer, Cham. https://doi.org/10.1007/978-3-319-10404-1_66
Download citation
DOI: https://doi.org/10.1007/978-3-319-10404-1_66
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-10403-4
Online ISBN: 978-3-319-10404-1
eBook Packages: Computer ScienceComputer Science (R0)