用于降低胰腺分割中边缘不确定性的扩散概率多线索水平集方法
Diffusion Probabilistic Multi-cue Level Set for Reducing Edge Uncertainty in Pancreas Segmentation
AI总结：
针对胰腺分割定位难、边缘不准的问题，提出基于扩散概率模型的多线索水平集方法Diff-mcs，采用粗到细策略，在三个公开数据集上取得SOTA性能，降低边缘不确定性并提升分割精度。
摘要：
精准分割胰腺仍是一项巨大挑战。传统方法因胰腺体积小、结构扭曲，在语义定位方面存在困难；而深度学习方法因对比度低、器官重叠，在获取精准边缘方面面临挑战。为解决这些问题，我们提出一种基于扩散概率模型的多线索水平集方法，名为Diff-mcs。我们的方法采用由粗到细的分割策略。在粗分割阶段，我们使用扩散概率模型，将得到的概率分布同时作为水平集方法的初始定位和先验线索。在精细分割阶段，我们将先验线索与灰度线索、纹理线索相结合，通过最大化水平集曲线内外线索的概率分布差异来优化边缘。该方法在三个公开数据集上得到验证，取得了当前最优性能，能够获得更精准的分割结果，且分割边缘的不确定性更低。此外，我们开展了消融实验和不确定性分析，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。进一步而言，结合多种线索后，水平集方法能够更好地获取边缘，提升整体精度。我们的代码可在https://github.com/GOUYUEE/Diff-mcs获取。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
