Abstract
Every segmentation task is uncertain due to image resolution, artefacts, annotation protocol etc. Propagating those uncertainties in a segmentation pipeline can improve the segmentation. This article aims to assess if segmentation can benefit from uncertainty of an auxiliary unsupervised task - the reconstruction of the input image. This auxillary task could help the network focus on rare examples that are otherwise poorly segmented. The method was applied to segmentation of myocardial infarction areas on cardiac magnetic resonance images.
A. Faure and T. Crozier—Equally contributed.
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Acknowledgments
This work was partly funded by Netherlands Organisation for Scientific Research (NWO) VICI project VI.C.182.042.
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Camarasa, R., Faure, A., Crozier, T., Bos, D., de Bruijne, M. (2021). Uncertainty-Based Segmentation of Myocardial Infarction Areas on Cardiac MR Images. In: Puyol Anton, E., et al. Statistical Atlases and Computational Models of the Heart. M&Ms and EMIDEC Challenges. STACOM 2020. Lecture Notes in Computer Science(), vol 12592. Springer, Cham. https://doi.org/10.1007/978-3-030-68107-4_40
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DOI: https://doi.org/10.1007/978-3-030-68107-4_40
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