In this study, the main objective is to develop an algorithm capable of identifying and delineating tumor regions in breast ultrasound (BUS) and mammographic images. The technique employs two advanced deep learning architectures, namely U-Net and pretrained SAM, for tumor segmentation. The U-Net model is specifically designed for medical image segmentation and leverages its deep convolutional neural network framework to extract meaningful features from input images. On the other hand, the pretrained SAM architecture incorporates a mechanism to capture spatial dependencies and generate segmentation results. Evaluation is conducted on a diverse dataset containing annotated tumor regions in BUS and mammographic images, covering both benign and malignant tumors. This dataset enables a comprehensive assessment of the algorithm's performance across different tumor types. Results demonstrate that the U-Net model outperforms the pretrained SAM architecture in accurately identifying and segmenting tumor regions in both BUS and mammographic images. The U-Net exhibits superior performance in challenging cases involving irregular shapes, indistinct boundaries, and high tumor heterogeneity. In contrast, the pretrained SAM architecture exhibits limitations in accurately identifying tumor areas, particularly for malignant tumors and objects with weak boundaries or complex shapes. These findings highlight the importance of selecting appropriate deep learning architectures tailored for medical image segmentation. The U-Net model showcases its potential as a robust and accurate tool for tumor detection, while the pretrained SAM architecture suggests the need for further improvements to enhance segmentation performance.

翻译：本研究旨在开发一种能够识别并勾画乳腺超声（BUS）及钼靶图像中肿瘤区域的算法。该技术采用两种先进的深度学习架构——U-Net与预训练SAM模型——进行肿瘤分割。U-Net模型专为医学图像分割设计，利用其深度卷积神经网络框架从输入图像中提取有效特征。而预训练SAM架构则通过引入空间依赖关系捕获机制生成分割结果。研究采用包含BUS及钼靶图像中标注肿瘤区域的多样化数据集进行评估，涵盖良性与恶性肿瘤。该数据集支持对算法在不同肿瘤类型中的性能进行全面评估。结果表明，U-Net模型在准确识别和分割BUS及钼靶图像肿瘤区域方面优于预训练SAM架构。在处理不规则形状、边界模糊及高度异质性肿瘤等复杂病例时，U-Net展现出更优性能。相比之下，预训练SAM架构在准确识别肿瘤区域方面存在局限，尤其对恶性肿瘤及弱边界或复杂形状目标表现欠佳。这些发现凸显了针对医学图像分割选择合适深度学习架构的重要性。U-Net模型展现了作为肿瘤检测稳健且精准工具的潜力，而预训练SAM架构则提示需进一步改进以提升分割性能。