Cold SegDiffusion: A novel diffusion model for medical image segmentation

Abstract

Medical image segmentation is crucial in accurately identifying and delineating regions of interest in medical images, which can inform the diagnosis and treatment of various diseases. Therefore, developing high-performance computer-aided diagnosis systems for medical image segmentation has become a prominent focus within computer vision. With the development of deep learning, diffusion models have exhibited remarkable performance in medical image segmentation tasks. However, traditional segmentation diffusion models typically employ random Gaussian noise to generate segmentation masks, resulting in non-unique segmentation masks that fail to guarantee the reproducibility of segmentation results. To tackle this issue, this paper introduces a novel method, Cold SegDiffusion, for general medical image segmentation based on the diffusion model. In this method, medical image segmentation is conceptualized as a denoising problem. The segmentation masks covering medical images serve as input for the segmentation encoder, addressing the challenge of generating non-unique masks due to noise randomness. Additionally, the contrast enhancement module is designed to translate features into the frequency domain, addressing the issues of low contrast and boundary disappearance in medical images. Furthermore, the suggested conditional cross-attention module utilizes the conditional encoder and cross-attention weights to enhance important features of the segmentation encoder output, improving the network’s capacity to focus on target regions. The proposed method is validated across three medical image segmentation datasets with different modalities. Experimental results demonstrate that Cold SegDiffusion outperforms mainstream segmentation methods. The code is available at https://github.com/TimesXY/Cold-SegDiffusion.