Abstract
This paper proposes a method to segment the pectoralis muscles in CT scans of patients within a Bayesian framework. First, a subject-tailored probabilistic atlas is constructed using affine registered label-maps of subjects that are highly similar to the test subject from a database of pairwise registered training subjects. The likelihood is constructed using a multivariate distribution taking intensities and distance to the atlas into account. The posterior probability is used to drive a graph cuts segmentation for classifying the CT into left major, left minor, right major, right minor pectoralis and non-pectoralis taking neighborhood information into account. The probabilistic prior is built using 400 CT scans and the method is tested on 50 independent CT scans. Results are reported on each muscle separately and show a statistically significant improvement when the subject-tailored prior is incorporated into the model. This automatic method can be used to objectively and efficiently asses clinical outcomes for patients with COPD.
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Acknowledgements
This work has been supported by NIH NHLBI award number 1R01HL116931, 1R01HL122464-01A1 and K25-HL104085. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.
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Harmouche, R., Ross, J.C., Washko, G.R., San José Estépar, R. (2014). Pectoralis Muscle Segmentation on CT Images Based on Bayesian Graph Cuts with a Subject-Tailored Atlas. In: Menze, B., et al. Medical Computer Vision: Algorithms for Big Data. MCV 2014. Lecture Notes in Computer Science(), vol 8848. Springer, Cham. https://doi.org/10.1007/978-3-319-13972-2_4
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DOI: https://doi.org/10.1007/978-3-319-13972-2_4
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