Abstract
Selective and iterative method for performance level estimation (SIMPLE) is a multi-atlas segmentation technique that integrates atlas selection and label fusion that has proven effective for radiotherapy planning. Herein, we revisit atlas selection and fusion techniques in the context of segmenting the spleen in metastatic liver cancer patients with possible splenomegaly using clinically acquired computed tomography (CT). We re-derive the SIMPLE algorithm in the context of the statistical literature, and show that the atlas selection criteria rest on newly presented principled likelihood models. We show that SIMPLE performance can be improved by accounting for exogenous information through Bayesian priors (so called context learning). These innovations are integrated with the joint label fusion approach to reduce the impact of correlated errors among selected atlases. In a study of 65 subjects, the spleen was segmented with median Dice similarity coefficient of 0.93 and a mean surface distance error of 2.2 mm.
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Wolz, R., Chu, C., Misawa, K., Fujiwara, M., Mori, K., Rueckert, D.: Automated abdominal multi-organ segmentation with subject-specific atlas generation. IEEE Transactions on Medical Imaging 32, 1723–1730 (2013)
Sabuncu, M.R., Yeo, B.T., Van Leemput, K., Fischl, B., Golland, P.: A generative model for image segmentation based on label fusion. IEEE Transactions on Medical Imaging 29, 1714–1729 (2010)
Langerak, T.R., van der Heide, U.A., Kotte, A.N., Viergever, M.A., van Vulpen, M., Pluim, J.P.: Label fusion in atlas-based segmentation using a selective and iterative method for performance level estimation (SIMPLE). IEEE Transactions on Medical Imaging 29, 2000–2008 (2010)
Dice, L.R.: Measures of the amount of ecologic association between species. Ecology 26, 297–302 (1945)
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, 903–921 (2004)
Rohlfing, T., Brandt, R., Menzel, R., Maurer Jr., C.R.: Evaluation of atlas selection strategies for atlas-based image segmentation with application to confocal microscopy images of bee brains. NeuroImage 21, 1428–1442 (2004)
Wang, H., Suh, J.W., Das, S.R., Pluta, J., Craige, C., Yushkevich, P.A.: Multi-Atlas Segmentation with Joint Label Fusion. IEEE Transactions on Pattern Analysis and Machine Intelligence (2012)
Asman, A.J., Landman, B.A.: Robust statistical label fusion through COnsensus Level, Labeler Accuracy, and Truth Estimation (COLLATE). IEEE Transactions on Medical Imaging 30, 1779–1794 (2011)
Van Leemput, K., Maes, F., Vandermeulen, D., Suetens, P.: Automated model-based bias field correction of MR images of the brain. IEEE Trans. Med. Imaging 18, 885–896 (1999)
McAuliffe, M.J., Lalonde, F.M., McGarry, D., Gandler, W., Csaky, K., Trus, B.L.: Medical image processing, analysis and visualization in clinical research. In: Proceedings of the 14th IEEE Symposium on Computer-Based Medical Systems, pp. 381–386. IEEE (2001)
Rueckert, D., Sonoda, L.I., Hayes, C., Hill, D.L.G., Leach, M.O., Hawkes, D.J.: Nonrigid registration using free-form deformations: Application to breast MR images. IEEE Trans. Med. Imaging 18, 712–721 (1999)
Jorge Cardoso, M., Leung, K., Modat, M., Keihaninejad, S., Cash, D., Barnes, J., Fox, N.C., Ourselin, S.: STEPS: Similarity and Truth Estimation for Propagated Segmentations and its application to hippocampal segmentation and brain parcelation. Med. Image Anal. 17, 671–684 (2013)
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Xu, Z., Asman, A.J., Shanahan, P.L., Abramson, R.G., Landman, B.A. (2014). SIMPLE Is a Good Idea (and Better with Context Learning). 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_46
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DOI: https://doi.org/10.1007/978-3-319-10404-1_46
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