Medical image segmentation is a crucial task that relies on the ability to accurately identify and isolate regions of interest in images. Thereby, generative approaches allow to capture the statistical properties of segmentation masks that are dependent on the respective medical images. In this work we propose a conditional score-based generative modeling framework that leverages the signed distance function to represent an implicit and smoother distribution of segmentation masks. The score function of the conditional distribution of segmentation masks is learned in a conditional denoising process, which can be effectively used to generate accurate segmentation masks. Moreover, uncertainty maps can be generated, which can aid in further analysis and thus 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，其依赖于准确识别并分离图像中感兴趣区域的能力。生成式方法能够捕捉依赖于相应医学图像的分割掩模的统计特性。本文提出一种条件评分生成建模框架，利用符号距离函数来表示分割掩模的隐式且更平滑的分布。分割掩模条件分布的评分函数通过条件去噪过程进行学习，可有效用于生成精确的分割掩模。此外，还可生成不确定性图，这有助于进一步分析，从而增强预测鲁棒性。我们在公开的细胞核和腺体分割数据集上，从定性和定量两方面展示了所提方法具有竞争力的性能，凸显了其在医学图像分割应用中的潜在价值。