Abstract
Accurate and robust segmentation of small organs in whole-body MRI is difficult due to anatomical variation and class imbalance. Recent deep network based approaches have demonstrated promising performance on abdominal multi-organ segmentations. However, the performance on small organs is still suboptimal as these occupy only small regions of the whole-body volumes with unclear boundaries and variable shapes. A coarse-to-fine, hierarchical strategy is a common approach to alleviate this problem, however, this might miss useful contextual information. We propose a two-stage approach with weighting schemes based on auto-context and spatial atlas priors. Our experiments show that the proposed approach can boost the segmentation accuracy of multiple small organs in whole-body MRI scans.
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Acknowledgements
V. Valindria is supported by the Indonesia Endowment for Education (LPDP)- Indonesian Presidential PhD Scholarship programme. B. Glocker received funding from the European Research Council (ERC) under the EU’s Horizon 2020 research and innovation programme (grant agreement No 757173, project MIRA, ERC-2017-STG).
The MRI data has been collected as part of the MALIBO project funded by the Efficacy and Mechanism Evaluation (EME) Programme, an MRC and NIHR partnership (EME project 13/122/01). The views expressed in this publication are those of the authors and not necessarily those of the MRC, NHS, NIHR or the Department of Health.
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Valindria, V.V. et al. (2018). Small Organ Segmentation in Whole-Body MRI Using a Two-Stage FCN and Weighting Schemes. In: Shi, Y., Suk, HI., Liu, M. (eds) Machine Learning in Medical Imaging. MLMI 2018. Lecture Notes in Computer Science(), vol 11046. Springer, Cham. https://doi.org/10.1007/978-3-030-00919-9_40
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