## 4 Methodologies and Implementation
### Research on the Application of DDPM in Biomedical Image Segmentation
#### 4.1.2 Segmentation Algorithms Incorporating DDPM
[62] introduces Diff-UNet, a novel end-to-end framework for medical volumetric segmentation that integrates the diffusion model into a standard U-shaped architecture. This integration effectively extracts semantic information from input volumes, resulting in superior pixel-level representations for segmentation. The proposed Step-Uncertainty based Fusion (SUF) module enhances the robustness of the diffusion model's predictions. Experimental results on benchmark datasets demonstrate the superiority of Diff-UNet over state-of-the-art approaches, showcasing its potential to facilitate more precise and accurate diagnosis and treatment of medical conditions. The paper's contribution to the field of medical image segmentation is significant, and Diff-UNet stands out as a promising method for improving patient outcomes. Ref. [63] addresses the vulnerability of deep neural networks to adversarial examples and their impact on biomedical image segmentation models. The authors highlight the potential security risks associated with this vulnerability and emphasize the need to test the robustness of deep learning models, especially when deployed in clinical tasks. They point out that while most research on adversarial examples focuses on non-medical image datasets, medical image datasets are also susceptible to adversarial attacks. Ref. [64] introduces MedSegDiff, the first diffusion probabilistic model (DPM) based model for general medical image segmentation tasks. Leveraging dynamic conditional encoding and Feature Frequency Parser, MedSegDiff aims to enhance regional attention and eliminate high-frequency noise components in medical image segmentation. The authors highlight the success of MedSegDiff in optic cup segmentation, brain tumor segmentation, and thyroid nodule segmentation, demonstrating considerable improvements over traditional DPM. The results indicate that dynamic conditioning significantly enhancesperformance, with improvements of 2.1% in optic cup segmentation and 1.6% to 1.8% in low-contrast image segmentation tasks. Furthermore, the paper contextualizes MedSegDiff within the broader landscape of computer vision and pattern recognition, emphasizing its potential to revolutionize medical image segmentation. Ref. [65] presents an innovative approach to medical image segmentation, addressing the challenges of high noise, ambiguity, and uncertainty. The proposed VerseDiff-UNet framework integrates the DDPM into a standard U-shaped architecture, combining noise-added images with labeled masks to guide accurate diffusion direction. Additionally, a shape prior module efficiently extracts structural semantic information from input spine images, enabling more precise segmentation of anatomical structures. The method outperforms other state-of-the-art techniques in accuracy while preserving natural features and variations of anatomy. Overall, the VerseDiff-UNet framework demonstrates promising potential for improving medical image segmentation, particularly in the context of spine imaging, and warrants further exploration in other medical imaging modalities. Ref. [66] proposes a novel framework called ESDiff that integrates retinal image enhancement and vessel segmentation to accurately diagnose various diseases. The proposed approach utilizes a diffusion model-based framework for image enhancement and a modified UNet to obtain degradation factors that preserve pathological features and pertinent information. The authors conducted extensive experiments on publicly available fundus retinal datasets to demonstrate the effectiveness of ESDiff compared to state-of-the-art methods. The results show that ESDiff outperforms existing methods in terms of image enhancement and vessel segmentation, and it is capable of diagnosing retinal vessel issues, offering valuable support to healthcare professionals in disease diagnosis and potentially reducing the workload of clinical experts in the field of medicine.