随着深度学习（DL）技术的快速发展，医学图像分割取得了显着的进步。现有的基于深度学习的分割模型通常具有区分性；即，他们的目标是学习从输入图像到分割掩模的映射。然而，这些判别方法忽略了底层数据分布和内在类别特征，导致特征空间不稳定。在这项工作中，我们建议利用生成模型的基础数据分布知识来补充判别性分割方法。为此，我们提出了一种用于医学图像分割的新型混合扩散框架，称为 HiDiff，它可以协同现有判别分割模型和新生成扩散模型的优势。 HiDiff 包含两个关键组件：判别分割器和扩散细化器。首先，我们利用任何传统的训练分割模型作为判别分割器，它可以为扩散细化器提供先验分割掩模。其次，我们提出了一种新颖的二元伯努利扩散模型（BBDM）作为扩散细化器，它可以通过对底层数据分布进行建模来有效、高效且交互式地细化分割掩模。第三，我们以交替协作的方式训练分段器和BBDM，相互促进。针对腹部器官、脑肿瘤、息肉和视网膜血管分割数据集的广泛实验结果（涵盖四种广泛使用的模式）证明了 HiDiff 优于现有医学分割算法（包括最先进的 Transformer 和 Diffusion）的性能基于的。此外，HiDiff 擅长分割小对象并推广到新数据集。源代码可从 https://github.com/takimailto/HiDiff 获取。

Medical image segmentation has been significantly advanced with the rapid development of deep learning (DL) techniques. Existing DL-based segmentation models are typically discriminative; i.e., they aim to learn a mapping from the input image to segmentation masks. However, these discriminative methods neglect the underlying data distribution and intrinsic class characteristics, suffering from unstable feature space. In this work, we propose to complement discriminative segmentation methods with the knowledge of underlying data distribution from generative models. To that end, we propose a novel hybrid diffusion framework for medical image segmentation, termed HiDiff, which can synergize the strengths of existing discriminative segmentation models and new generative diffusion models. HiDiff comprises two key components: discriminative segmentor and diffusion refiner. First, we utilize any conventional trained segmentation models as discriminative segmentor, which can provide a segmentation mask prior for diffusion refiner. Second, we propose a novel binary Bernoulli diffusion model (BBDM) as the diffusion refiner, which can effectively, efficiently, and interactively refine the segmentation mask by modeling the underlying data distribution. Third, we train the segmentor and BBDM in an alternate-collaborative manner to mutually boost each other. Extensive experimental results on abdomen organ, brain tumor, polyps, and retinal vessels segmentation datasets, covering four widely-used modalities, demonstrate the superior performance of HiDiff over existing medical segmentation algorithms, including the state-of-the-art transformer- and diffusion-based ones. In addition, HiDiff excels at segmenting small objects and generalizing to new datasets. Source codes are made available at https://github.com/takimailto/HiDiff.