scholarly journals 3D Breast Image Registration — A Review

2005 ◽  
Vol 4 (1) ◽  
pp. 39-48 ◽  
Author(s):  
Radhika Sivaramakrishna
Keyword(s):  
Image Registration ◽  
Breast Imaging ◽  
Control Point ◽  
Breast Mri ◽  
Breast Ultrasound ◽  
Ultrasound Images ◽  
Post Contrast ◽  
Positron Emission ◽  
Breast Image ◽  
Primary Focus

Image registration is an important problem in breast imaging. It is used in a wide variety of applications that include better visualization of lesions on pre- and post-contrast breast MRI images, speckle tracking and image compounding in breast ultrasound images, alignment of positron emission, and standard mammography images on hybrid machines et cetera. It is a prerequisite to align images taken at different times to isolate small interval lesions. Image registration also has useful applications in monitoring cancer therapy. The field of breast image registration has gained considerable interest in recent years. While the primary focus of interest continues to be the registration of pre- and post-contrast breast MRI images, other areas like breast ultrasound registration have gained more attention in recent years. The focus of registration algorithms has also shifted from control point based semiautomated techniques, to more sophisticated voxel based automated techniques that use mutual information as a similarity measure. This paper visits the problem of breast image registration and provides an overview of the current state-of-the-art in this area.

scholarly journals A Novel Computer-Aided-Diagnosis System for Breast Ultrasound Images Based on BI-RADS Categories

2020 ◽  
Vol 10 (5) ◽  
pp. 1830
Author(s):  
Yi-Wei Chang ◽  
Yun-Ru Chen ◽  
Chien-Chuan Ko ◽  
Wei-Yang Lin ◽  
Keng-Pei Lin
Keyword(s):  
Breast Tumor ◽  
Breast Imaging ◽  
Breast Lesion ◽  
Low Cost ◽  
Breast Ultrasound ◽  
Tissue Imaging ◽  
Support Vector ◽  
Ultrasound Images ◽  
Traditional Assessment ◽  
Cad System

The breast ultrasound is not only one of major devices for breast tissue imaging, but also one of important methods in breast tumor screening. It is non-radiative, non-invasive, harmless, simple, and low cost screening. The American College of Radiology (ACR) proposed the Breast Imaging Reporting and Data System (BI-RADS) to evaluate far more breast lesion severities compared to traditional diagnoses according to five-criterion categories of masses composition described as follows: shape, orientation, margin, echo pattern, and posterior features. However, there exist some problems, such as intensity differences and different resolutions in image acquisition among different types of ultrasound imaging modalities so that clinicians cannot always identify accurately the BI-RADS categories or disease severities. To this end, this article adopted three different brands of ultrasound scanners to fetch breast images for our experimental samples. The breast lesion was detected on the original image using preprocessing, image segmentation, etc. The breast tumor’s severity was evaluated on the features of the breast lesion via our proposed classifiers according to the BI-RADS standard rather than traditional assessment on the severity; i.e., merely using benign or malignant. In this work, we mainly focused on the BI-RADS categories 2–5 after the stage of segmentation as a result of the clinical practice. Moreover, several features related to lesion severities based on the selected BI-RADS categories were introduced into three machine learning classifiers, including a Support Vector Machine (SVM), Random Forest (RF), and Convolution Neural Network (CNN) combined with feature selection to develop a multi-class assessment of breast tumor severity based on BI-RADS. Experimental results show that the proposed CAD system based on BI-RADS can obtain the identification accuracies with SVM, RF, and CNN reaching 80.00%, 77.78%, and 85.42%, respectively. We also validated the performance and adaptability of the classification using different ultrasound scanners. Results also indicate that the evaluations of F-score based on CNN can obtain measures higher than 75% (i.e., prominent adaptability) when samples were tested on various BI-RADS categories.

Breast Imaging Reporting and Data System (BI-RADS)

2018 ◽  
Author(s):  
Carl D’Orsi
Keyword(s):  
Cancer Detection ◽  
Breast Imaging ◽  
False Negative ◽  
Breast Mri ◽  
Breast Ultrasound ◽  
Cancer Detection Rate ◽  
Data System ◽  
True Positive ◽  
True Negative ◽  
Predictive Values

This chapter, devoted to the Breast Imaging Reporting and Data System (BI-RADS), describes the standardized language applied to findings in mammography, breast ultrasound, and breast MRI. BI-RADS terms most frequently used are described, and most are illustrated by figures. In addition, the rules for a facility and radiologist audit are described, with definitions of true positive (TP), false positive (FP), true negative (TN), and false negative (FN) findings. Sensitivity (Se), specificity (Sp), positive predictive values 1, 2, and 3 (PPV1, 2, 3), and cancer detection rate are defined. An example of an audit is provided to clarify the use of these metrics.

scholarly journals Incorporating the Breast Imaging Reporting and Data System Lexicon with a Fully Convolutional Network for Malignancy Detection on Breast Ultrasound

Diagnostics ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 66
Author(s):  
Yung-Hsien Hsieh ◽  
Fang-Rong Hsu ◽  
Seng-Tong Dai ◽  
Hsin-Ya Huang ◽  
Dar-Ren Chen ◽  
...  
Keyword(s):  
Deep Learning ◽  
Breast Imaging ◽  
Area Under The Curve ◽  
Semantic Segmentation ◽  
Breast Ultrasound ◽  
Data System ◽  
Ultrasound Images ◽  
Convolutional Network ◽  
Learning Network ◽  
Deep Learning Network

In this study, we applied semantic segmentation using a fully convolutional deep learning network to identify characteristics of the Breast Imaging Reporting and Data System (BI-RADS) lexicon from breast ultrasound images to facilitate clinical malignancy tumor classification. Among 378 images (204 benign and 174 malignant images) from 189 patients (102 benign breast tumor patients and 87 malignant patients), we identified seven malignant characteristics related to the BI-RADS lexicon in breast ultrasound. The mean accuracy and mean IU of the semantic segmentation were 32.82% and 28.88, respectively. The weighted intersection over union was 85.35%, and the area under the curve was 89.47%, showing better performance than similar semantic segmentation networks, SegNet and U-Net, in the same dataset. Our results suggest that the utilization of a deep learning network in combination with the BI-RADS lexicon can be an important supplemental tool when using ultrasound to diagnose breast malignancy.

scholarly journals Clinical practice guidelines for high-resolution breast PET, 2019 edition

2021 ◽  
Vol 35 (3) ◽  
pp. 406-414
Author(s):  
Yoko Satoh ◽  
Masami Kawamoto ◽  
Kazunori Kubota ◽  
Koji Murakami ◽  
Makoto Hosono ◽  
...  
Keyword(s):  
High Resolution ◽  
Practice Guidelines ◽  
Breast Imaging ◽  
Japanese Society ◽  
Insurance Coverage ◽  
Whole Body ◽  
Positron Emission Mammography ◽  
Dedicated Breast Pet ◽  
Positron Emission ◽  
Different Types

AbstractBreast positron emission tomography (PET) has had insurance coverage when performed with conventional whole-body PET in Japan since 2013. Together with whole-body PET, accurate examination of breast cancer and diagnosis of metastatic disease are possible, and are expected to contribute significantly to its treatment planning. To facilitate a safer, smoother, and more appropriate examination, the Japanese Society of Nuclear Medicine published the first edition of practice guidelines for high-resolution breast PET in 2013. Subsequently, new types of breast PET have been developed and their clinical usefulness clarified. Therefore, the guidelines for breast PET were revised in 2019. This article updates readers as to what is new in the second edition. This edition supports two different types of breast PET depending on the placement of the detector: the opposite-type (positron emission mammography; PEM) and the ring-shaped type (dedicated breast PET; dbPET), providing an overview of these scanners and appropriate imaging methods, their clinical applications, and future prospects. The name “dedicated breast PET” from the first edition is widely used to refer to ring-shaped type breast PET. In this edition, “breast PET” has been defined as a term that refers to both opposite- and ring-shaped devices. Up-to-date breast PET practice guidelines would help provide useful information for evidence-based breast imaging.

Nipple Localization in Automated Whole Breast Ultrasound Coronal Scans Using Ensemble Learning

2020 ◽  
Vol 43 (1) ◽  
pp. 29-45
Author(s):  
Alex Noel Joseph Raj ◽  
Ruban Nersisson ◽  
Vijayalakshmi G. V. Mahesh ◽  
Zhemin Zhuang
Keyword(s):  
Texture Features ◽  
Breast Ultrasound ◽  
Support Vector ◽  
Ultrasound Images ◽  
Shadow Area ◽  
Hu Moments ◽  
Artificial Neural Network Ann ◽  
Cade System ◽  
Occurrence Matrix ◽  
Shape Complexity

Nipple is a vital landmark in the breast lesion diagnosis. Although there are advanced computer-aided detection (CADe) systems for nipple detection in breast mediolateral oblique (MLO) views of mammogram images, few academic works address the coronal views of breast ultrasound (BUS) images. This paper addresses a novel CADe system to locate the Nipple Shadow Area (NSA) in ultrasound images. Here the Hu Moments and Gray-level Co-occurrence Matrix (GLCM) were calculated through an iterative sliding window for the extraction of shape and texture features. These features are then concatenated and fed into an Artificial Neural Network (ANN) to obtain probable NSA’s. Later, contour features, such as shape complexity through fractal dimension, edge distance from the periphery and contour area, were computed and passed into a Support Vector Machine (SVM) to identify the accurate NSA in each case. The coronal plane BUS dataset is built upon our own, which consists of 64 images from 13 patients. The test results show that the proposed CADe system achieves 91.99% accuracy, 97.55% specificity, 82.46% sensitivity and 88% F-score on our dataset.

Breast image registration by using non-linear local affine transformation

2013 ◽  
Author(s):  
Feiyu Chen ◽  
Peng Zheng ◽  
Penglong Xu ◽  
Andrew D. A. Maidment ◽  
Predrag R. Bakic ◽  
...  
Keyword(s):  
Image Registration ◽  
Affine Transformation ◽  
Breast Image ◽  
Non Linear

Molecular Breast Imaging: A Scientific Review

2021 ◽  
Author(s):  
Katie N Hunt
Keyword(s):  
Breast Cancer ◽  
Breast Imaging ◽  
Breast Mri ◽  
Cancer Staging ◽  
Scientific Review ◽  
Molecular Breast Imaging ◽  
Breast Cancer Staging ◽  
Fast Learning ◽  
Technical Advances ◽  
History Of

Abstract Molecular breast imaging (MBI) is a nuclear medicine technique that has evolved considerably over the past two decades. Technical advances have allowed reductions in administered doses to the point that they are now acceptable for screening. The most common radiotracer used in MBI, 99mTc-sestamibi, has a long history of safe use. Biopsy capability has become available in recent years, with early clinical experience demonstrating technically successful biopsies of MBI-detected lesions. MBI has been shown to be an effective supplemental screening tool in women with dense breasts and is also utilized for breast cancer staging, assessment of response to neoadjuvant chemotherapy, problem solving, and as an alternative to breast MRI in women who have a contraindication to MRI. The degree of background parenchymal uptake on MBI shows promise as a tool for breast cancer risk stratification. Radiologist interpretation is guided by a validated MBI lexicon that mirrors the BI-RADS lexicon. With short interpretation times, a fast learning curve for radiologists, and a substantially lower cost than breast MRI, MBI provides many benefits in the practices in which it is utilized. This review will discuss the current state of MBI technology, clinical applications of MBI, MBI interpretation, radiation dose associated with MBI, and the future of MBI.

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