  Diffusion probabilistic model (DPM) recently becomes one of the hottest topic
in computer vision. Its image generation application such as Imagen, Latent
Diffusion Models and Stable Diffusion have shown impressive generation
capabilities, which aroused extensive discussion in the community. Many recent
studies also found it is useful in many other vision tasks, like image
deblurring, super-resolution and anomaly detection. Inspired by the success of
DPM, we propose the first DPM based model toward general medical image
segmentation tasks, which we named MedSegDiff. In order to enhance the
step-wise regional attention in DPM for the medical image segmentation, we
propose dynamic conditional encoding, which establishes the state-adaptive
conditions for each sampling step. We further propose Feature Frequency Parser
(FF-Parser), to eliminate the negative effect of high-frequency noise component
in this process. We verify MedSegDiff on three medical segmentation tasks with
different image modalities, which are optic cup segmentation over fundus
images, brain tumor segmentation over MRI images and thyroid nodule
segmentation over ultrasound images. The experimental results show that
MedSegDiff outperforms state-of-the-art (SOTA) methods with considerable
performance gap, indicating the generalization and effectiveness of the
proposed model. Our code is released at https://github.com/WuJunde/MedSegDiff.
