Diffusion Probabilistic Multi-cue Level Set for Reducing Edge
  Uncertainty in Pancreas Segmentation
Yue Gou Yuming Xing Shengzhu Shi Zhichang Guo
Accurately segmenting the pancreas remains a huge challenge. Traditional
methods encounter difficulties in semantic localization due to the small volume
and distorted structure of the pancreas, while deep learning methods encounter
challenges in obtaining accurate edges because of low contrast and organ
overlapping. To overcome these issues, we propose a multi-cue level set method
based on the diffusion probabilistic model, namely Diff-mcs. Our method adopts
a coarse-to-fine segmentation strategy. We use the diffusion probabilistic
model in the coarse segmentation stage, with the obtained probability
distribution serving as both the initial localization and prior cues for the
level set method. In the fine segmentation stage, we combine the prior cues
with grayscale cues and texture cues to refine the edge by maximizing the
difference between probability distributions of the cues inside and outside the
level set curve. The method is validated on three public datasets and achieves
state-of-the-art performance, which can obtain more accurate segmentation
results with lower uncertainty segmentation edges. In addition, we conduct
ablation studies and uncertainty analysis to verify that the diffusion
probability model provides a more appropriate initialization for the level set
method. Furthermore, when combined with multiple cues, the level set method can
better obtain edges and improve the overall accuracy. Our code is available at
https://github.com/GOUYUEE/Diff-mcs.