  Recent trends in semi-supervised learning have significantly boosted the
performance of 3D semi-supervised medical image segmentation. Compared with 2D
images, 3D medical volumes involve information from different directions, e.g.,
transverse, sagittal, and coronal planes, so as to naturally provide
complementary views. These complementary views and the intrinsic similarity
among adjacent 3D slices inspire us to develop a novel annotation way and its
corresponding semi-supervised model for effective segmentation. Specifically,
we firstly propose the orthogonal annotation by only labeling two orthogonal
slices in a labeled volume, which significantly relieves the burden of
annotation. Then, we perform registration to obtain the initial pseudo labels
for sparsely labeled volumes. Subsequently, by introducing unlabeled volumes,
we propose a dual-network paradigm named Dense-Sparse Co-training (DeSCO) that
exploits dense pseudo labels in early stage and sparse labels in later stage
and meanwhile forces consistent output of two networks. Experimental results on
three benchmark datasets validated our effectiveness in performance and
efficiency in annotation. For example, with only 10 annotated slices, our
method reaches a Dice up to 86.93% on KiTS19 dataset.
