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:医学图象分解是一项关键任务,取决于能否准确识别和隔离图像中感兴趣的区域。因此,基因化方法能够捕捉取决于相关医疗图象的分解面罩的统计特性。我们在此工作中提议一个有条件的分分解模型框架,利用已签字的距离功能来代表分解面罩的隐含和平稳分布。有条件分解面罩分布的评分功能在有条件的分解过程中学习,可以有效地用于生成准确的分解面罩。此外,可以生成不确定性地图,有助于进一步分析,从而增强预测性强度。我们从质量和数量上说明拟议方法在公共核和分解面数据集上的竞争性表现,突出其在医学图象分解应用中的潜在作用。</s>