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
this https URL

准确地划分胰腺仍然是一个巨大的挑战。为了克服传统方法和深度学习方法在语义定位和边缘精确性方面的困难，我们提出了一种基于扩散概率模型的多线索水平集方法，命名为 Diff-mcs。通过在粗分割阶段中应用扩散概率模型，我们使用获得的概率分布作为初始定位和先验线索，在精细分割阶段中，我们结合灰度线索和纹理线索以通过最大化线索内外的概率分布差异来改进边缘。该方法在三个公共数据集上验证并取得了最先进的性能，能够获得更准确的分割结果和较低不确定性的分割边缘。此外，我们还进行了剥离研究和不确定性分析，验证了扩散概率模型为水平集方法提供了更合适的初始化。此外，当与多个线索结合时，水平集方法可以更好地获得边缘并提高整体准确性。