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：本研究的主要目标是开发一种能够在乳腺超声（BUS）及钼靶图像中识别并勾勒肿瘤区域的算法。该技术采用两种先进的深度学习架构，即U-Net和预训练SAM，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意义的特征。而预训练SAM架构则整合了一种捕捉空间依赖性并生成分割结果的机制。在包含BUS和钼靶图像中标注肿瘤区域的多样化数据集上进行了评估，涵盖了良性和恶性肿瘤。该数据集能够全面评估算法在不同肿瘤类型上的性能。结果表明，在准确识别和分割BUS及钼靶图像中的肿瘤区域方面，U-Net模型优于预训练SAM架构。在处理不规则形状、边界模糊及肿瘤异质性高的复杂病例时，U-Net展现出更优越的性能。相比之下，预训练SAM架构在准确识别肿瘤区域（尤其针对恶性肿瘤以及弱边界或复杂形态的目标）方面存在局限性。这些发现突显了针对医学图像分割量身选择合适的深度学习架构的重要性。U-Net模型展现了其作为稳健且精确肿瘤检测工具的潜力，而预训练SAM架构则表明其需要进一步改进以提升分割性能。