Medical image segmentation is a crucial task that relies on the ability to accurately identify and isolate regions of interest in medical images. Thereby, generative approaches allow to capture the statistical properties of segmentation masks that are dependent on the respective structures. In this work we propose a conditional score-based generative modeling framework to represent the signed distance function (SDF) leading to an implicit distribution of segmentation masks. The advantage of leveraging the SDF is a more natural distortion when compared to that of binary masks. By learning the score function of the conditional distribution of SDFs we can accurately sample from the distribution of segmentation masks, allowing for the evaluation of statistical quantities. Thus, this probabilistic representation allows for the generation of uncertainty maps represented by the variance, which can aid in further analysis and enhance the predictive robustness. We qualitatively and quantitatively illustrate competitive performance of the proposed method on a public nuclei and gland segmentation data set, highlighting its potential utility in medical image segmentation applications.

翻译：医学图像分割是一项关键任务，依赖于准确识别和分离医学图像中感兴趣区域的能力。其中，生成方法能够捕获依赖于各自结构的分割掩膜的统计特性。本文提出一种基于条件分数的生成建模框架，用以表示符号距离函数，从而隐式地生成分割掩膜分布。利用符号距离函数的优势在于，与二值掩膜相比，其变形更自然。通过学习符号距离函数条件分布的分数函数，我们可以从分割掩膜分布中精确采样，进而评估统计量。因此，这种概率表示能够生成以方差表示的不确定性图，有助于进一步分析并增强预测鲁棒性。我们通过定性和定量方式，在公开的细胞核和腺体分割数据集上展示了所提方法的竞争性能，突显其在医学图像分割应用中的潜在价值。