Abstract
Segmentation of anatomical structures and pathologies is inherently ambiguous. For instance, structure borders may not be clearly visible or different experts may have different styles of annotating. The majority of current state-of-the-art methods do not account for such ambiguities but rather learn a single mapping from image to segmentation. In this work, we propose a novel method to model the conditional probability distribution of the segmentations given an input image. We derive a hierarchical probabilistic model, in which separate latent variables are responsible for modelling the segmentation at different resolutions. Inference in this model can be efficiently performed using the variational autoencoder framework. We show that our proposed method can be used to generate significantly more realistic and diverse segmentation samples compared to recent related work, both, when trained with annotations from a single or multiple annotators. The code for this paper is freely available at https://github.com/baumgach/PHiSeg-code.
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Acknowledgements
This work was partially supported by the Swiss Data Science Center. One of the Titan X Pascal used for this research was donated by the NVIDIA Corporation.
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Baumgartner, C.F. et al. (2019). PHiSeg: Capturing Uncertainty in Medical Image Segmentation. In: Shen, D., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science(), vol 11765. Springer, Cham. https://doi.org/10.1007/978-3-030-32245-8_14
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DOI: https://doi.org/10.1007/978-3-030-32245-8_14
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