Computer-Aided Multiview Tumor Detection for Automated Whole Breast Ultrasound

Lo, Chiao; Shen, Yanchang; Huang, Chiun‐Sheng; Chang, Ruey-Feng

doi:10.1177/0161734613507240

Cited by 40 publications

(38 citation statements)

References 46 publications

(59 reference statements)

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“…Multiview cancer detection is an interactive region for the tumor contender which focus better for tumor growth phase. [7] …”

Section: Multiview Tumor Detectionmentioning

confidence: 99%

A Study of Computerized Techniques Used in Ultrasound Breast Cancer Detection

Rani¹,

Dhenakaren²

2015

IJCA

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Staging of cancer is said to be unsigned in natural history. In this advanced growth of medical research, finding out the tumor growth is demanding in the midst of the researchers. The aim of this study is to provide a pathway for predicting tumor stages with the help of processing tools. Since breast cancer tumor is said to in the worlds second place of disease, prevention of higher growth sound be maintained. With sticking out to reduce costs and high predicted upshot ultrasound images, were chosen. Ultrasound screening images are targeted to produce an ending over currently predicting techniques. Breast cancer tumor growth findings lead to the public to get aware of frequent possible medical checkup. Overcoming the margins of existing techniques and methods, a new approach is stretched out for prediction phase. Neural networks and image segmentation based concepts are composed and a final wrapping up will lead advanced finding of efficiency. This study starts from the collection of ultrasound images, followed by the methods that define for predicting the cancer tumor, and the limitations to be met.

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“…Multiview cancer detection is an interactive region for the tumor contender which focus better for tumor growth phase. [7] …”

Section: Multiview Tumor Detectionmentioning

confidence: 99%

A Study of Computerized Techniques Used in Ultrasound Breast Cancer Detection

Rani¹,

Dhenakaren²

2015

IJCA

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“…Due to the increasing use of 3D breast ultrasound imaging for disease diagnosis, computer assistance if even more effective in helping clinicians evaluate the images [7]. However, manually segmenting whole 3D ultrasound volumes is an exhausting process and clinically impractical [8].…”

Section: Introductionmentioning

confidence: 99%

Automated 3D ultrasound image segmentation to aid breast cancer image interpretation

Lee

Roubidoux

et al. 2016

Ultrasonics

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Segmentation of an ultrasound image into functional tissues is of great importance to clinical diagnosis of breast cancer. However, many studies are found to segment only the mass of interest and not all major tissues. Differences and inconsistencies in ultrasound interpretation call for an automated segmentation method to make results operator-independent. Furthermore, manual segmentation of entire three-dimensional (3D) ultrasound volumes is time-consuming, resource-intensive, and clinically impractical. Here, we propose an automated algorithm to segment 3D ultrasound volumes into three major tissue types: cyst/mass, fatty tissue, and fibro-glandular tissue. To test its efficacy and consistency, the proposed automated method was employed on a database of 21 cases of whole breast ultrasound. Experimental results show that our proposed method not only distinguishes fat and non-fat tissues correctly, but performs well in classifying cyst/mass. Comparison of density assessment between the automated method and manual segmentation demonstrates good consistency with an accuracy of 85.7%. Quantitative comparison of corresponding tissue volumes, which uses overlap ratio, gives an average similarity of 74.54%, consistent with values seen in MRI brain segmentations. Thus, our proposed method exhibits great potential as an automated approach to segment 3D whole breast ultrasound volumes into functionally distinct tissues that may help to correct ultrasound speed of sound aberrations and assist in density based prognosis of breast cancer.

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“…Some of these concerns may be alleviated with the use of new automated whole-breast ultrasound systems. 35,41,[43][44][45][46][47][48][49][50] There is also strong support for MRI, [51][52][53][54][55][56][57][58] but there remain concerns about the use of contrast agents, its cost and its availability, although accelerated breast MRI sequences are being trialed. [59][60][61][62][63][64][65][66][67][68][69] Finally, molecular breast imaging is being touted for extremely dense breasts, [70][71][72][73][74][75][76][77][78] although concerns persist about dose and contrast; nonetheless, these concerns are starting to be addressed.…”

Section: Introductionmentioning

confidence: 99%

Vision 20/20: Mammographic breast density and its clinical applications

Lau

2015

Medical Physics

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Breast density is a strong predictor of the failure of mammography screening to detect breast cancer and is a strong predictor of the risk of developing breast cancer. The many imaging options that are now available for imaging dense breasts show great promise, but there is still the question of determining which women are "dense" and what imaging modality is suitable for individual women. To date, mammographic breast density has been classified according to the Breast Imaging-Reporting and Data System (BI-RADS) categories from visual assessment, but this is known to be very subjective. Despite many research reports, the authors believe there has been a lack of physics-led and evidence-based arguments about what breast density actually is, how it should be measured, and how it should be used. In this paper, the authors attempt to start correcting this situation by reviewing the history of breast density research and the debates generated by the advocacy movement. The authors review the development of breast density estimation from pattern analysis to area-based analysis, and the current automated volumetric breast density (VBD) analysis. This is followed by a discussion on seeking the ground truth of VBD and mapping volumetric methods to BI-RADS density categories. The authors expect great improvement in VBD measurements that will satisfy the needs of radiologists, epidemiologists, surgeons, and physicists. The authors believe that they are now witnessing a paradigm shift toward personalized breast screening, which is going to see many more cancers being detected early, with the use of automated density measurement tools as an important component.

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Computer-Aided Multiview Tumor Detection for Automated Whole Breast Ultrasound

Cited by 40 publications

References 46 publications

A Study of Computerized Techniques Used in Ultrasound Breast Cancer Detection

A Study of Computerized Techniques Used in Ultrasound Breast Cancer Detection

Automated 3D ultrasound image segmentation to aid breast cancer image interpretation

Vision 20/20: Mammographic breast density and its clinical applications

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