ProSegDiff: Prostate Segmentation Diffusion Network Based on Adaptive Adjustment of Injection Features

Abstract

Recently, methods based on Diffusion Probability Models (DPM) have achieved notable success in the field of medical image segmentation. However, most of these methods do not perform well in segmenting ambiguous areas when dealing with prostate segmentation tasks due to the low distinguishability of prostate images and the high overlap of its boundary with adjacent organs. To address this issue, this paper introduces a diffusion-based framework named ProSegDiff, ProSegDiff employs an Adapter to dynamically adjust features from the conditional network to align with the denoising process of the denoising network. Furthermore, the denoising process is conducted in the latent space to minimize the consumption of computational resources, and a proposed selection strategy is employed to identify the better results from multiple inferences. Extensive comparative experiments on four benchmark datasets demonstrate the effectiveness of this method, which achieves superior performance across four evaluation metrics.