{"title":"CBAM-RIUnet: Breast Tumor Segmentation With Enhanced Breast Ultrasound and Test-Time Augmentation","authors":"Lal Omega Boro, Gypsy Nandi","doi":"10.1177/01617346241276411","DOIUrl":null,"url":null,"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.","PeriodicalId":49401,"journal":{"name":"Ultrasonic Imaging","volume":null,"pages":null},"PeriodicalIF":2.5000,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Ultrasonic Imaging","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1177/01617346241276411","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ACOUSTICS","Score":null,"Total":0}
引用次数: 0
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.
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