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 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.
翻译:最近的许多研究还发现,在很多其它的视觉任务中,例如图像破碎、超分辨率和异常探测中,它非常有用。在DPM的成功启发下,我们提出了第一个基于一般医疗图像分割任务的DPM模型,我们将其命名为MedSegDiffiff。为了在DPM中加强对医疗图像分割的渐进式区域关注,我们提出了动态有条件编码,为每个取样步骤设定了州适应性条件。我们进一步提议了Fater Riotive Passer(FF-Parster),以消除此过程中高频噪音组成部分的消极影响。我们核实了MedSegDiff在三种医学分解任务上采用不同图像模式,即光学杯分解,脑肿瘤分解出MRI图像和超超声波图像的甲状腺结核分解,我们提出了动态有条件编码,为每个取样步骤设定了适合国家适应性条件。我们进一步提议了Paterrentral Passer(FF-Parster),以消除此过程中高频噪音组成部分的负面效应。我们核实了MegetDiff关于三种医疗分解任务的三个医学任务,这三种不同图像模式,即对Fundus图像的分解为光学图像、脑隔断、脑图像的大脑图像、脑分解和超声压、超声压、实验结果显示法显示。