Abstract
In many medical imaging applications, merging segmentations obtained from multiple reference images (i.e., templates) has become a standard practice for improving the accuracy as well as reliability. Simultaneous Truth And Performance Level Estimation (STAPLE) is a widely used fusion algorithm that simultaneously estimates both performance parameters for each template, and the output segmentation; a more accurate estimation of performance parameters consequently results in more accurate output segmentations. In this paper, we propose a new approach for learning prior knowledge about the performance parameters of each template, and for incorporating it into the Maximum-a-Posteriori (MAP) formulation of the STAPLE, so that more accurate output segmentations can be obtained. More specifically, we propose a new approach to learn, for each structure to be segmented, the relationships between the performance parameters (viz. sensitivity and specificity) and the intensity similarities; we also propose a methodology for transferring this prior knowledge about the performance parameters into the STAPLE algorithm through optimal setting of the MAP parameters. The proposed approach is evaluated for the segmentation of structures in the brain MR images. These experiments have clearly demonstrated the advantages of incorporating such prior knowledge.
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Warfield, S., Zou, K., Wells, W.: Simultaneous truth and performance level estimation (STAPLE): an algorithm for the validation of image segmentation. IEEE Transactions on Medical Imaging 23(7), 903–921 (2004)
Akhondi-Asl, A., Warfield, S.: Simultaneous truth and performance level estimation through fusion of probabilistic segmentations. IEEE Transactions on Medical Imaging 32(10), 1840–1852 (2013)
Asman, A., Landman, B.: Formulating spatially varying performance in the statistical fusion framework. IEEE Transactions on Medical Imaging 31(6), 1326–1336 (2012)
Artaechevarria, X., Munoz-Barrutia, A.: Combination strategies in multi-atlas image segmentation: Application to brain MR data. IEEE Transactions on Medical Imaging 28(8), 1266–1277 (2009)
Sabuncu, M., Yeo, B., Van Leemput, K., Fischl, B., Golland, P.: A generative model for image segmentation based on label fusion. IEEE Transactions on Medical Imaging 29(99), 1714–1729 (2010)
Wang, H., Suh, J., Das, S., Pluta, J., Craige, C., Yushkevich, P.: Multi-atlas segmentation with joint label fusion. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(3), 611–623 (2013)
Gorthi, S., Bach Cuadra, M., Tercier, P.A., Allal, A., Thiran, J.P.: Weighted shape-based averaging with neighborhood prior model for multiple atlas fusion-based medical image segmentation. IEEE Signal Processing Letters 20(11), 1034–1037 (2013)
Cardoso, M., Leung, K., Modat, M., Barnes, J., Ourselin, S.: Locally ranked STAPLE for template based segmentation propagation. In: Workshop on Multi-Atlas Labeling and Statistical Fusion (2011)
Commowick, O., Warfield, S.K.: Incorporating priors on expert performance parameters for segmentation validation and label fusion: A maximum a posteriori STAPLE. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds.) MICCAI 2010, Part III. LNCS, vol. 6363, pp. 25–32. Springer, Heidelberg (2010)
Commowick, O., Akhondi-Asl, A., Warfield, S.K.: Estimating a reference standard segmentation with spatially varying performance parameters: Local MAP STAPLE. IEEE Transactions on Medical Imaging 31(8), 1593–1606 (2012)
AbouRizk, S.M., Halpin, D.W., Wilson, J.R.: Visual interactive fitting of beta distributions. Journal of Construction Engineering and Management 117(4), 589–605 (1991)
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Gorthi, S., Akhondi-Asl, A., Thiran, JP., Warfield, S.K. (2014). Optimal MAP Parameters Estimation in STAPLE - Learning from Performance Parameters versus Image Similarity Information. In: Wu, G., Zhang, D., Zhou, L. (eds) Machine Learning in Medical Imaging. MLMI 2014. Lecture Notes in Computer Science, vol 8679. Springer, Cham. https://doi.org/10.1007/978-3-319-10581-9_22
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DOI: https://doi.org/10.1007/978-3-319-10581-9_22
Publisher Name: Springer, Cham
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