BGDiff: Boundary-Guided Injection Diffusion Framework for Prostate Segmentation

Jialong Zhong, Tingwei Liu, Miao Zhang, Yongri Piao, Weibing Sun, Huchuan Lu

Abstract: Recently, the Diffusion Probabilistic Model (DPM)-based methods have achieved substantial success in the field of medical image segmentation. However, most of these methods are not effective in addressing the issue of blurred edges in prostate segmentation tasks. To address this issue, This paper proposes a framework based on the diffusion model, named BGDiff, which is based on Boundary Guided Injection Module(BGIM) and Adaptive Boundary Loss for prostate segmentation. The BGIM can establish connections between the denoising processes of adjacent steps, thereby providing stable guidance for boundary areas as the denoising progresses step by step, while the Adaptive Boundary Loss adjusts the loss weights for more challenging boundaries based on the model’s active feedback. Extensive experiments on four benchmark datasets demonstrate the effectiveness of the proposed method and achieve state-of-the-art performance on four evaluation metrics. The source code will be publicly available at https://github.com/zjlGO/BGDiff