Diffusion model-based medical ultrasound segmentation network in ultrasound image

In medical ultrasound image segmentation, lesion areas are often blurred, making it difficult to distinguish them from the background, thereby complicating segmentation tasks. In the past decade, deep convolutional neural networks have proven effective for medical image segmentation. However, the inductive biases in convolutional architectures limit their ability to capture long-range dependencies. Recently, denoising diffusion probabilistic models (DDPMs) have emerged as powerful generative frameworks in computer vision. Yet, many diffusion-based segmentation approaches overlook the semantic relationships between lesion regions (foreground) and surrounding normal tissues (background), often resulting in distorted segmentation outputs. To address these limitations, we propose DMUS-Net, a diffusion model-based network for medical ultrasound segmentation. DMUS-Net integrates a Multi-Scale Conditional Guidance Network (MSCGN) and Adaptive Detail-Oriented Attention (AODA) modules. By Leveraging the Transformer network’s global relational capabilities, DMUS-Net effectively balances attention between global context and fine-grained features. Subsequently, it dynamically integrates rich image prior information, enhancing semantic correlations between foreground and background. Additionally, we introduce Context-Aware Cross-Decoding layers (CACD) to capture global features and inter-channel correlations, thereby improving both segmentation accuracy and efficiency. DMUS-Net is applied to ultrasound segmentation tasks, including breast, thyroid, and gallbladder stones, achieving superior results, in comparative experiments. These findings highlight DMUS-Net’s robust generalization ability and potential for practical clinical applications. • We proposed DMUS-Net: a DDPM-based model for ultrasound image segmentation. • We designed a Multi-Scale Conditional Guidance Network (MSCGN) to enhance segmentation performance. • We validated DMUS-Net’s effectiveness and generalization across multiple ultrasound tasks and datasets.