Introducing Shape Prior Module in Diffusion Model for Medical Image
  Segmentation
Zhiqing Zhang Guojia Fan Tianyong Liu Nan Li Yuyang Liu Ziyu Liu Canwei Dong Shoujun Zhou
Medical image segmentation is critical for diagnosing and treating spinal
disorders. However, the presence of high noise, ambiguity, and uncertainty
makes this task highly challenging. Factors such as unclear anatomical
boundaries, inter-class similarities, and irrational annotations contribute to
this challenge. Achieving both accurate and diverse segmentation templates is
essential to support radiologists in clinical practice. In recent years,
denoising diffusion probabilistic modeling (DDPM) has emerged as a prominent
research topic in computer vision. It has demonstrated effectiveness in various
vision tasks, including image deblurring, super-resolution, anomaly detection,
and even semantic representation generation at the pixel level. Despite the
robustness of existing diffusion models in visual generation tasks, they still
struggle with discrete masks and their various effects. To address the need for
accurate and diverse spine medical image segmentation templates, we propose an
end-to-end framework called VerseDiff-UNet, which leverages the denoising
diffusion probabilistic model (DDPM). Our approach integrates the diffusion
model into a standard U-shaped architecture. At each step, we combine the
noise-added image with the labeled mask to guide the diffusion direction
accurately towards the target region. Furthermore, to capture specific
anatomical a priori information in medical images, we incorporate a shape a
priori module. This module efficiently extracts structural semantic information
from the input spine images. We evaluate our method on a single dataset of
spine images acquired through X-ray imaging. Our results demonstrate that
VerseDiff-UNet significantly outperforms other state-of-the-art methods in
terms of accuracy while preserving the natural features and variations of
anatomy.