Omega Boro Lal, Nandi Gypsy
Department of Computer Applications, Assam Don Bosco University, Guwahati, India.
Ultrason Imaging. 2025 Jan;47(1):24-36. doi: 10.1177/01617346241276411. Epub 2024 Sep 16.
This study addresses the challenge of precise breast tumor segmentation in ultrasound images, crucial for effective Computer-Aided Diagnosis (CAD) in breast cancer. We introduce CBAM-RIUnet, a deep learning (DL) model for automated breast tumor segmentation in breast ultrasound (BUS) images. The model, featuring an efficient convolutional block attention module residual inception Unet, outperforms existing models, particularly excelling in Dice and IoU scores. CBAM-RIUnet follows the Unet structure with a residual inception depth-wise separable convolution, and incorporates a convolutional block attention module (CBAM) to eliminate irrelevant features and focus on the region of interest. Evaluation under three scenarios, including enhanced breast ultrasound (EBUS) and test-time augmentation (TTA), demonstrates impressive results. CBAM-RIUnet achieves Dice and IoU scores of 89.38% and 88.71%, respectively, showcasing significant improvements compared to state-of-the-art DL techniques. In conclusion, CBAM-RIUnet presents a highly effective and simplified DL model for breast tumor segmentation in BUS imaging.
本研究应对了超声图像中精确乳腺肿瘤分割的挑战，这对乳腺癌的有效计算机辅助诊断（CAD）至关重要。我们引入了CBAM-RIUnet，这是一种用于乳腺超声（BUS）图像中自动乳腺肿瘤分割的深度学习（DL）模型。该模型具有高效的卷积块注意力模块残差 inception Unet，优于现有模型，尤其在Dice和IoU分数方面表现出色。CBAM-RIUnet采用具有残差 inception 深度可分离卷积的Unet结构，并结合了卷积块注意力模块（CBAM）来消除无关特征并专注于感兴趣区域。在包括增强型乳腺超声（EBUS）和测试时增强（TTA）在内的三种场景下进行评估，结果令人印象深刻。CBAM-RIUnet的Dice和IoU分数分别达到89.38%和88.71%，与最先进的DL技术相比有显著改进。总之，CBAM-RIUnet为BUS成像中的乳腺肿瘤分割提供了一种高效且简化的DL模型。
