Volumetric Medical Image Segmentation via Scribble Annotations and Shape
  Priors
Qiuhui Chen Haiying Lyu Xinyue Hu Yong Lu Yi Hong
Recently, weakly-supervised image segmentation using weak annotations like
scribbles has gained great attention in computer vision and medical image
analysis, since such annotations are much easier to obtain compared to
time-consuming and labor-intensive labeling at the pixel/voxel level. However,
due to a lack of structure supervision on regions of interest (ROIs), 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 each image slice. In
this paper, we propose a scribble-based volumetric image segmentation,
Scribble2D5, which tackles 3D anisotropic image segmentation and aims to its
improve 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 use a combination of static and active boundary prediction to
learn ROI's boundary and regularize its shape. Also, we propose an optional
add-on component, which incorporates the shape prior information from unpaired
segmentation masks to further improve model accuracy. Extensive experiments on
three public datasets and one private dataset demonstrate our Scribble2D5
achieves state-of-the-art performance on volumetric image segmentation using
scribbles and shape prior if available.