Abstract：
Medical image segmentation demands precise accuracy and the capability to assess segmentation uncertainty for informed clinical decision-making. Denoising Diffusion Probability Models (DDPMs), with their advancements in image generation, can treat segmentation as a conditional generation task, providing accurate segmentation and uncertainty estimation. However, current DDPMs used in medical image segmentation suffer from low inference efficiency and prediction errors caused by excessive noise at the end of the forward process. To address this issue, we propose an accelerated denoising diffusion probabilistic model via truncated inverse processes (ADDPM) that is specifically designed for medical image segmentation. The inverse process of ADDPM starts from a non-Gaussian distribution and terminates early once a prediction with relatively low noise is obtained after multiple iterations of denoising. We employ a separate powerful segmentation network to obtain pre-segmentation and construct the non-Gaussian distribution of the segmentation based on the forward diffusion rule. By further adopting a separate denoising network, the final segmentation can be obtained with just one denoising step from the predictions with low noise. ADDPM greatly reduces the number of denoising steps to approximately one-tenth of that in vanilla DDPMs. Our experiments on four segmentation tasks demonstrate that ADDPM outperforms both vanilla DDPMs and existing representative accelerating DDPMs methods. Moreover, ADDPM can be easily integrated with existing advanced segmentation models to improve segmentation performance and provide uncertainty estimation. Implementation code: https://github.com/Guoxt/ADDPM.
摘要：
医学图像分割需要精确的准确性和评估分割不确定性的能力，以便做出明智的临床决策。去噪扩散概率模型(DDPM)，随着他们在图像生成方面的进步，可以将分割视为条件生成任务，提供准确的分割和不确定性估计。然而,当前用于医学图像分割的DDPM在正向处理结束时由于过多的噪声而导致推理效率低和预测误差。为了解决这个问题，我们提出了一种通过截断逆过程（ADDPM）的加速去噪扩散概率模型，该模型是专门为医学图像分割而设计的。ADDPM的逆过程从非高斯分布开始,并且一旦在多次迭代去噪之后获得具有相对低噪声的预测,就提前终止。我们采用单独的强大分割网络来获得预分割，并基于前向扩散规则构造分割的非高斯分布。通过进一步采用单独的去噪网络，从低噪声的预测中只需一个去噪步骤就可以获得最终的分割。ADDPM大大减少了去噪步骤的数量，约为香草DDPM的十分之一。我们对四个分割任务的实验表明，ADDPM优于香草DDPM和现有的代表性加速DDPM方法。此外，ADDPM可以轻松地与现有的高级分割模型集成，以提高分割性能并提供不确定性估计。实现代码：https://github.com/Guoxt/ADDPM。
