Medical image segmentation is a challenging task with inherent ambiguity and high uncertainty, attributed to factors such as unclear tumor boundaries and multiple plausible annotations. The accuracy and diversity of segmentation masks are both crucial for providing valuable references to radiologists in clinical practice. While existing diffusion models have shown strong capacities in various visual generation tasks, it is still challenging to deal with discrete masks in segmentation. To achieve accurate and diverse medical image segmentation masks, we propose a novel conditional Bernoulli Diffusion model for medical image segmentation (BerDiff). Instead of using the Gaussian noise, we first propose to use the Bernoulli noise as the diffusion kernel to enhance the capacity of the diffusion model for binary segmentation tasks, resulting in more accurate segmentation masks. Second, by leveraging the stochastic nature of the diffusion model, our BerDiff randomly samples the initial Bernoulli noise and intermediate latent variables multiple times to produce a range of diverse segmentation masks, which can highlight salient regions of interest that can serve as valuable references for radiologists. In addition, our BerDiff can efficiently sample sub-sequences from the overall trajectory of the reverse diffusion, thereby speeding up the segmentation process. Extensive experimental results on two medical image segmentation datasets with different modalities demonstrate that our BerDiff outperforms other recently published state-of-the-art methods. Our results suggest diffusion models could serve as a strong backbone for medical image segmentation.

翻译：医学图像分割是一项具有挑战性的任务，由于肿瘤边界不清晰以及存在多种合理标注等因素，导致其具有固有的模糊性和高不确定性。分割掩码的准确性和多样性对于为临床放射科医生提供有价值的参考都至关重要。虽然现有扩散模型在各种视觉生成任务中展现出强大能力，但在处理分割中的离散掩码时仍面临挑战。为实现准确且多样的医学图像分割掩码，我们提出了一种新颖的面向医学图像分割的条件伯努利扩散模型（BerDiff）。首先，我们提出使用伯努利噪声替代高斯噪声作为扩散核，以增强扩散模型在二值分割任务中的能力，从而生成更准确的分割掩码。其次，通过利用扩散模型的随机特性，我们的BerDiff可多次随机采样初始伯努利噪声和中间潜变量，生成一系列多样化的分割掩码，这些掩码能够突出显示关键感兴趣区域，为放射科医生提供有价值的参考。此外，我们的BerDiff能够高效地从逆向扩散的整体轨迹中采样子序列，从而加速分割过程。在两个不同模态的医学图像分割数据集上的大量实验结果表明，我们的BerDiff优于其他近期发表的先进方法。我们的研究结果表明，扩散模型可作为医学图像分割的强大主干网络。