Abstract
The objective of this work is to segment any arbitrary structures of interest (SOI) in 3D volumes by only annotating a single slice, (i.e. semi-automatic 3D segmentation). We show that high accuracy can be achieved by simply propagating the 2D slice segmentation with an affinity matrix between consecutive slices, which can be learnt in a self-supervised manner, namely slice reconstruction. Specifically, we compare our proposed framework, termed as Sli2Vol, with supervised approaches and two other unsupervised/self-supervised slice registration approaches, on 8 public datasets (both CT and MRI scans), spanning 9 different SOIs. Without any parameter-tuning, the same model achieves superior performance with Dice scores (0–100 scale) of over 80 for most of the benchmarks, including the ones that are unseen during training. Our results show generalizability of the proposed approach across data from different machines and with different SOIs: a major use case of semi-automatic segmentation methods where fully supervised approaches would normally struggle.
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Acknowledgments
PH. Yeung is grateful for support from the RC Lee Centenary Scholarship. A. Namburete is funded by the UK Royal Academy of Engineering under its Engineering for Development Research Fellowship scheme. W. Xie is supported by the UK Engineering and Physical Sciences Research Council (EPSRC) Programme Grant Seebibyte (EP/M013774/1) and Grant Visual AI (EP/T028572/1). We thank Madeleine Wyburd and Nicola Dinsdale for their valuable suggestions and comments about the work.
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Yeung, PH., Namburete, A.I.L., Xie, W. (2021). Sli2Vol: Annotate a 3D Volume from a Single Slice with Self-supervised Learning. In: de Bruijne, M., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2021. MICCAI 2021. Lecture Notes in Computer Science(), vol 12902. Springer, Cham. https://doi.org/10.1007/978-3-030-87196-3_7
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