Oza Urvi, Gohel Bakul, Kumar Pankaj, Oza Parita
Computer Science Dhirubhai Ambani Institute of Information and Communication Technology, Gandhinagar, Gujarat, India.
Computer Science & Engineering Nirma University, Ahmedabad, Gujarat, India.
Int J Biomed Imaging. 2024 Aug 9;2024:9422083. doi: 10.1155/2024/9422083. eCollection 2024.
Accurate segmentation of breast masses in mammogram images is essential for early cancer diagnosis and treatment planning. Several deep learning (DL) models have been proposed for whole mammogram segmentation and mass patch/crop segmentation. However, current DL models for breast mammogram mass segmentation face several limitations, including false positives (FPs), false negatives (FNs), and challenges with the end-to-end approach. This paper presents a novel two-stage end-to-end cascaded breast mass segmentation framework that incorporates a saliency map of potential mass regions to guide the DL models for breast mass segmentation. The first-stage segmentation model of the cascade framework is used to generate a saliency map to establish a coarse region of interest (ROI), effectively narrowing the focus to probable mass regions. The proposed presegmenter attention (PSA) blocks are introduced in the second-stage segmentation model to enable dynamic adaptation to the most informative regions within the mammogram images based on the generated saliency map. Comparative analysis of the Attention U-net model with and without the cascade framework is provided in terms of dice scores, precision, recall, FP rates (FPRs), and FN outcomes. Experimental results consistently demonstrate enhanced breast mass segmentation performance by the proposed cascade framework across all three datasets: INbreast, CSAW-S, and DMID. The cascade framework shows superior segmentation performance by improving the dice score by about 6% for the INbreast dataset, 3% for the CSAW-S dataset, and 2% for the DMID dataset. Similarly, the FN outcomes were reduced by 10% for the INbreast dataset, 19% for the CSAW-S dataset, and 4% for the DMID dataset. Moreover, the proposed cascade framework's performance is validated with varying state-of-the-art segmentation models such as DeepLabV3+ and Swin transformer U-net. The presegmenter cascade framework has the potential to improve segmentation performance and mitigate FNs when integrated with any medical image segmentation framework, irrespective of the choice of the model.
在乳腺钼靶图像中准确分割乳腺肿块对于早期癌症诊断和治疗规划至关重要。已经提出了几种深度学习（DL）模型用于全乳腺钼靶分割和肿块补丁/裁剪分割。然而，当前用于乳腺钼靶肿块分割的DL模型面临一些局限性，包括假阳性（FPs）、假阴性（FNs）以及端到端方法的挑战。本文提出了一种新颖的两阶段端到端级联乳腺肿块分割框架，该框架结合了潜在肿块区域的显著性图来指导用于乳腺肿块分割的DL模型。级联框架的第一阶段分割模型用于生成显著性图以建立粗略的感兴趣区域（ROI），有效地将关注点缩小到可能的肿块区域。在第二阶段分割模型中引入了所提出的预分割器注意力（PSA）块，以基于生成的显著性图动态适应乳腺钼靶图像中信息最丰富的区域。在骰子分数、精度、召回率、FP率（FPRs）和FN结果方面，提供了对有无级联框架的注意力U-net模型的比较分析。实验结果一致表明，所提出的级联框架在所有三个数据集（INbreast、CSAW-S和DMID）上均增强了乳腺肿块分割性能。级联框架通过将INbreast数据集的骰子分数提高约6%、CSAW-S数据集提高3%、DMID数据集提高2%，显示出卓越的分割性能。同样，INbreast数据集的FN结果减少了10%，CSAW-S数据集减少了19%，DMID数据集减少了4%。此外，所提出的级联框架的性能通过与不同的先进分割模型（如DeepLabV3+和Swin变压器U-net）进行验证。预分割器级联框架在与任何医学图像分割框架集成时，无论模型选择如何，都有潜力提高分割性能并减轻FNs。
