  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 the 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.
The source code will be made publicly available at
https://github.com/pakheiyeung/Sli2Vol.
