D-DDPM: Deep Denoising Diffusion Probabilistic Models for Lesion Segmentation and Data Generation in Ultrasound Imaging

IF 3.4 3区 计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS IEEE Access Pub Date : 2025-03-05 DOI:10.1109/ACCESS.2025.3548128
Abdalrahman Alblwi;Saleh Makkawy;Kenneth E. Barner
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Abstract

The Denoising Diffusion Probabilistic Model (DDPM) has gained significant attention for its powerful image generation and segmentation capabilities, particularly in biomedical applications where accuracy is critical. In breast cancer detection, ultrasound imaging is widely used due to its safety, affordability, and non-ionizing nature. However, the inherent challenges of ultrasound data, such as noise and artifacts, make accurate tumor segmentation difficult, often leading to misdiagnosis. We propose a novel Deep Denoising Probabilistic Diffusion Model (D-DDPM) designed to enhance tumor segmentation in breast ultrasound images to address these limitations. Our model incorporates a nested U-Net architecture with Residual U-blocks (RSU), significantly improving feature learning and segmentation precision. In addition to performing segmentation, D-DDPM generates synthetic data, augmenting existing real datasets to improve data size with a diverse range of high-quality samples. We validated D-DDPM on several breast ultrasound datasets, comparing its performance to state-of-the-art methods. The proposed D-DDPM achieves a Dice score improvement of 2.26%, 4.24%, and 5% over the runner-up model, demonstrating superior performance on all BUS datasets. Both qualitative and quantitative results demonstrate the ability of D-DDPM to deliver more accurate and reliable segmentation results, offering promising potential to improve clinical decision-making in cancer diagnosis.
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去噪扩散概率模型(DDPM)因其强大的图像生成和分割能力而备受关注,尤其是在精度至关重要的生物医学应用中。在乳腺癌检测中,超声成像因其安全性、经济性和非电离性而得到广泛应用。然而,超声波数据固有的挑战,如噪声和伪影,使准确的肿瘤分割变得困难,往往导致误诊。我们提出了一种新颖的深度去噪概率扩散模型(D-DDPM),旨在增强乳腺超声图像中的肿瘤分割,以解决这些局限性。我们的模型采用了嵌套 U-Net 架构和残余 U-blocks (RSU),显著提高了特征学习和分割精度。除了进行分割外,D-DDPM 还能生成合成数据,扩充现有的真实数据集,从而利用各种高质量样本改善数据规模。我们在多个乳腺超声数据集上验证了 D-DDPM,并将其性能与最先进的方法进行了比较。提议的 D-DDPM 比亚军模型的 Dice 分数分别提高了 2.26%、4.24% 和 5%,在所有 BUS 数据集上都表现出了卓越的性能。定性和定量结果都表明,D-DDPM 能够提供更准确、更可靠的分割结果,有望改善癌症诊断的临床决策。
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来源期刊
IEEE Access
IEEE Access COMPUTER SCIENCE, INFORMATION SYSTEMSENGIN-ENGINEERING, ELECTRICAL & ELECTRONIC
CiteScore
9.80
自引率
7.70%
发文量
6673
审稿时长
6 weeks
期刊介绍: IEEE Access® is a multidisciplinary, open access (OA), applications-oriented, all-electronic archival journal that continuously presents the results of original research or development across all of IEEE''s fields of interest. IEEE Access will publish articles that are of high interest to readers, original, technically correct, and clearly presented. Supported by author publication charges (APC), its hallmarks are a rapid peer review and publication process with open access to all readers. Unlike IEEE''s traditional Transactions or Journals, reviews are "binary", in that reviewers will either Accept or Reject an article in the form it is submitted in order to achieve rapid turnaround. Especially encouraged are submissions on: Multidisciplinary topics, or applications-oriented articles and negative results that do not fit within the scope of IEEE''s traditional journals. Practical articles discussing new experiments or measurement techniques, interesting solutions to engineering. Development of new or improved fabrication or manufacturing techniques. Reviews or survey articles of new or evolving fields oriented to assist others in understanding the new area.
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