Zhun Xie , Jiaqi Han , Nan Ji , Lijun Xu , Jianguo Ma
{"title":"利用频谱增强射频信号分割超声图像的 RFImageNet 框架","authors":"Zhun Xie , Jiaqi Han , Nan Ji , Lijun Xu , Jianguo Ma","doi":"10.1016/j.ultras.2024.107498","DOIUrl":null,"url":null,"abstract":"<div><div>Computer-aided segmentation of medical ultrasound images assists in medical diagnosis, promoting accuracy and reducing the burden of sonographers. However, the existing ultrasonic intelligent segmentation models are mainly based on B-mode grayscale images, which lack sufficient clarity and contrast compared to natural images. Previous research has indicated that ultrasound radiofrequency (RF) signals contain rich spectral information that could be beneficial for tissue recognition but is lost in grayscale images. In this paper, we introduce an image segmentation framework, RFImageNet, that leverages spectral and amplitude information from RF signals to segment ultrasound image. Firstly, the positive and negative values in the RF signal are separated into the red and green channels respectively in the proposed RF image, ensuring the preservation of frequency information. Secondly, we developed a deep learning model, RFNet, tailored to the specific input image size requirements. Thirdly, RFNet was trained using RF images with spectral data augmentation and tested against other models. The proposed method achieved a mean intersection over union (mIoU) of 54.99% and a dice score of 63.89% in the segmentation of rat abdominal tissues, as well as a mIoU of 63.28% and a dice score of 68.92% in distinguishing between benign and malignant breast tumors. These results highlight the potential of combining RF signals with deep learning algorithms for enhanced diagnostic capabilities.</div></div>","PeriodicalId":23522,"journal":{"name":"Ultrasonics","volume":"146 ","pages":"Article 107498"},"PeriodicalIF":3.8000,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"RFImageNet framework for segmentation of ultrasound images with spectra-augmented radiofrequency signals\",\"authors\":\"Zhun Xie , Jiaqi Han , Nan Ji , Lijun Xu , Jianguo Ma\",\"doi\":\"10.1016/j.ultras.2024.107498\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Computer-aided segmentation of medical ultrasound images assists in medical diagnosis, promoting accuracy and reducing the burden of sonographers. However, the existing ultrasonic intelligent segmentation models are mainly based on B-mode grayscale images, which lack sufficient clarity and contrast compared to natural images. Previous research has indicated that ultrasound radiofrequency (RF) signals contain rich spectral information that could be beneficial for tissue recognition but is lost in grayscale images. In this paper, we introduce an image segmentation framework, RFImageNet, that leverages spectral and amplitude information from RF signals to segment ultrasound image. Firstly, the positive and negative values in the RF signal are separated into the red and green channels respectively in the proposed RF image, ensuring the preservation of frequency information. Secondly, we developed a deep learning model, RFNet, tailored to the specific input image size requirements. Thirdly, RFNet was trained using RF images with spectral data augmentation and tested against other models. The proposed method achieved a mean intersection over union (mIoU) of 54.99% and a dice score of 63.89% in the segmentation of rat abdominal tissues, as well as a mIoU of 63.28% and a dice score of 68.92% in distinguishing between benign and malignant breast tumors. These results highlight the potential of combining RF signals with deep learning algorithms for enhanced diagnostic capabilities.</div></div>\",\"PeriodicalId\":23522,\"journal\":{\"name\":\"Ultrasonics\",\"volume\":\"146 \",\"pages\":\"Article 107498\"},\"PeriodicalIF\":3.8000,\"publicationDate\":\"2024-10-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Ultrasonics\",\"FirstCategoryId\":\"101\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0041624X24002610\",\"RegionNum\":2,\"RegionCategory\":\"物理与天体物理\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ACOUSTICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Ultrasonics","FirstCategoryId":"101","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0041624X24002610","RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ACOUSTICS","Score":null,"Total":0}
RFImageNet framework for segmentation of ultrasound images with spectra-augmented radiofrequency signals
Computer-aided segmentation of medical ultrasound images assists in medical diagnosis, promoting accuracy and reducing the burden of sonographers. However, the existing ultrasonic intelligent segmentation models are mainly based on B-mode grayscale images, which lack sufficient clarity and contrast compared to natural images. Previous research has indicated that ultrasound radiofrequency (RF) signals contain rich spectral information that could be beneficial for tissue recognition but is lost in grayscale images. In this paper, we introduce an image segmentation framework, RFImageNet, that leverages spectral and amplitude information from RF signals to segment ultrasound image. Firstly, the positive and negative values in the RF signal are separated into the red and green channels respectively in the proposed RF image, ensuring the preservation of frequency information. Secondly, we developed a deep learning model, RFNet, tailored to the specific input image size requirements. Thirdly, RFNet was trained using RF images with spectral data augmentation and tested against other models. The proposed method achieved a mean intersection over union (mIoU) of 54.99% and a dice score of 63.89% in the segmentation of rat abdominal tissues, as well as a mIoU of 63.28% and a dice score of 68.92% in distinguishing between benign and malignant breast tumors. These results highlight the potential of combining RF signals with deep learning algorithms for enhanced diagnostic capabilities.
期刊介绍:
Ultrasonics is the only internationally established journal which covers the entire field of ultrasound research and technology and all its many applications. Ultrasonics contains a variety of sections to keep readers fully informed and up-to-date on the whole spectrum of research and development throughout the world. Ultrasonics publishes papers of exceptional quality and of relevance to both academia and industry. Manuscripts in which ultrasonics is a central issue and not simply an incidental tool or minor issue, are welcomed.
As well as top quality original research papers and review articles by world renowned experts, Ultrasonics also regularly features short communications, a calendar of forthcoming events and special issues dedicated to topical subjects.