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- W3201756488 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." @default.
- W3201756488 created "2021-10-11" @default.
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- W3201756488 date "2021-01-01" @default.
- W3201756488 modified "2023-10-14" @default.
- W3201756488 title "Sli2Vol: Annotate a 3D Volume from a Single Slice with Self-supervised Learning" @default.
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- W3201756488 doi "https://doi.org/10.1007/978-3-030-87196-3_7" @default.
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