3D Medical Image Segmentation with Sparse Annotation via Cross-Teaching
  between 3D and 2D Networks
Heng Cai Lei Qi Qian Yu Yinghuan Shi Yang Gao
Medical image segmentation typically necessitates a large and precisely
annotated dataset. However, obtaining pixel-wise annotation is a
labor-intensive task that requires significant effort from domain experts,
making it challenging to obtain in practical clinical scenarios. In such
situations, reducing the amount of annotation required is a more practical
approach. One feasible direction is sparse annotation, which involves
annotating only a few slices, and has several advantages over traditional weak
annotation methods such as bounding boxes and scribbles, as it preserves exact
boundaries. However, learning from sparse annotation is challenging due to the
scarcity of supervision signals. To address this issue, we propose a framework
that can robustly learn from sparse annotation using the cross-teaching of both
3D and 2D networks. Considering the characteristic of these networks, we
develop two pseudo label selection strategies, which are hard-soft confidence
threshold and consistent label fusion. Our experimental results on the MMWHS
dataset demonstrate that our method outperforms the state-of-the-art (SOTA)
semi-supervised segmentation methods. Moreover, our approach achieves results
that are comparable to the fully-supervised upper bound result.