Recently, the Diffusion Probabilistic Model (DPM)-based methods have achieved
substantial success in the field of medical image segmentation. However, most
of these methods fail to enable the diffusion model to learn edge features and
non-edge features effectively and to inject them efficiently into the diffusion
backbone. Additionally, the domain gap between the images features and the
diffusion model features poses a great challenge to prostate segmentation. In
this paper, we proposed CriDiff, a two-stage feature injecting framework with a
Crisscross Injection Strategy (CIS) and a Generative Pre-train (GP) approach
for prostate segmentation. The CIS maximizes the use of multi-level features by
efficiently harnessing the complementarity of high and low-level features. To
effectively learn multi-level of edge features and non-edge features, we
proposed two parallel conditioners in the CIS: the Boundary Enhance Conditioner
(BEC) and the Core Enhance Conditioner (CEC), which discriminatively model the
image edge regions and non-edge regions, respectively. Moreover, the GP
approach eases the inconsistency between the images features and the diffusion
model without adding additional parameters. Extensive experiments on four
benchmark datasets demonstrate the effectiveness of the proposed method and
achieve state-of-the-art performance on four evaluation metrics.

本文提出了一个两阶段的特征注入框架 CriDiff，其中包括 Crisscross Injection Strategy (CIS) 和 Generative Pre-train (GP) 方法，用于前列腺分割。CIS 通过高效地利用高和低级特征的互补性来最大化多级特征的使用。通过 CIS 中的两个并行条件器：边界增强条件器 (BEC) 和核心增强条件器 (CEC)，可以有效地学习多级边缘特征和非边缘特征。此外，GP 方法可减少图像特征与扩散模型之间的不一致性，而不添加额外参数。在四个基准数据集上进行了大量实验证明了所提方法的有效性，并在四个评估指标上实现了最先进的性能。