CBAM-RIUnet: Breast Tumor Segmentation With Enhanced Breast Ultrasound and Test-Time Augmentation

IF 2.5 4区 医学 Q1 ACOUSTICS Ultrasonic Imaging Pub Date : 2024-09-17 DOI:10.1177/01617346241276411
Lal Omega Boro, Gypsy Nandi
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Abstract

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.
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CBAM-RIUnet:利用增强型乳腺超声波和增强测试时间进行乳腺肿瘤分割
本研究解决了在超声图像中精确分割乳腺肿瘤的难题,这对有效的乳腺癌计算机辅助诊断(CAD)至关重要。我们介绍了 CBAM-RIUnet,这是一种用于在乳腺超声(BUS)图像中自动分割乳腺肿瘤的深度学习(DL)模型。该模型具有高效的卷积块注意模块残差(residual inception Unet),性能优于现有模型,尤其是在Dice和IoU得分方面表现突出。CBAM-RIUnet 遵循 Unet 结构,具有残差起始深度可分离卷积,并结合了卷积块注意模块 (CBAM),以消除无关特征并聚焦于感兴趣的区域。在增强乳腺超声(EBUS)和测试时间增强(TTA)等三种情况下进行的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 技术相比有显著提高。总之,CBAM-RIUnet 为 BUS 成像中的乳腺肿瘤分割提供了一个高效、简化的 DL 模型。
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来源期刊
Ultrasonic Imaging
Ultrasonic Imaging 医学-工程:生物医学
CiteScore
5.10
自引率
8.70%
发文量
15
审稿时长
>12 weeks
期刊介绍: Ultrasonic Imaging provides rapid publication for original and exceptional papers concerned with the development and application of ultrasonic-imaging technology. Ultrasonic Imaging publishes articles in the following areas: theoretical and experimental aspects of advanced methods and instrumentation for imaging
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