  Recently, weakly-supervised image segmentation using weak annotations like
scribbles has gained great attention, since such annotations are much easier to
obtain compared to time-consuming and label-intensive labeling at the
pixel/voxel level. However, because scribbles lack structure information of
region of interest (ROI), existing scribble-based methods suffer from poor
boundary localization. Furthermore, most current methods are designed for 2D
image segmentation, which do not fully leverage the volumetric information if
directly applied to image slices. In this paper, we propose a scribble-based
volumetric image segmentation, Scribble2D5, which tackles 3D anisotropic image
segmentation and improves boundary prediction. To achieve this, we augment a
2.5D attention UNet with a proposed label propagation module to extend semantic
information from scribbles and a combination of static and active boundary
prediction to learn ROI's boundary and regularize its shape. Extensive
experiments on three public datasets demonstrate Scribble2D5 significantly
outperforms current scribble-based methods and approaches the performance of
fully-supervised ones. Our code is available online.
