Elongated Physiological Structure Segmentation via Spatial and Scale
  Uncertainty-aware Network
Yinglin Zhang Ruiling Xi Huazhu Fu Dave Towey RuiBin Bai Risa Higashita Jiang Liu
Robust and accurate segmentation for elongated physiological structures is
challenging, especially in the ambiguous region, such as the corneal
endothelium microscope image with uneven illumination or the fundus image with
disease interference. In this paper, we present a spatial and scale
uncertainty-aware network (SSU-Net) that fully uses both spatial and scale
uncertainty to highlight ambiguous regions and integrate hierarchical structure
contexts. First, we estimate epistemic and aleatoric spatial uncertainty maps
using Monte Carlo dropout to approximate Bayesian networks. Based on these
spatial uncertainty maps, we propose the gated soft uncertainty-aware (GSUA)
module to guide the model to focus on ambiguous regions. Second, we extract the
uncertainty under different scales and propose the multi-scale
uncertainty-aware (MSUA) fusion module to integrate structure contexts from
hierarchical predictions, strengthening the final prediction. Finally, we
visualize the uncertainty map of final prediction, providing interpretability
for segmentation results. Experiment results show that the SSU-Net performs
best on cornea endothelial cell and retinal vessel segmentation tasks.
Moreover, compared with counterpart uncertainty-based methods, SSU-Net is more
accurate and robust.