Computer aided detection in automated 3-D breast ultrasound images: a survey

Kozegar, Ehsan; Soryani, Mohsen; Behnam, Hamid; Salamati, Masoumeh; Tan, Tao

doi:10.1007/s10462-019-09722-7

Cited by 32 publications

(14 citation statements)

References 44 publications

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“…As main complementary examination to mammography, US was much superior to mammography due to its attractive advantages including, but not limited to, high sensitivity especially in dense breasts, are unexposed to X-rays and are safe for young, pregnant, lactating population (28,29).…”

Section: Discussionmentioning

confidence: 99%

“…Nevertheless, conventional US manual scanning of breast have some limitations such as operator dependency and poor reproducibility. What is worse, simultaneous gathering and interpretation of images made radiologists exhausted and may increase the missing rate of cancers (28). ML is a sub-field of artificial intelligence, which allows computers to learn from data without being explicitly programmed (30), and shows promising advantages in fast, accurate, friendly use and low-cost detection of targets in medical imaging.…”

Section: Discussionmentioning

confidence: 99%

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Study on automatic detection and classification of breast nodule using deep convolutional neural network system

Wang¹,

Liu²,

Yuan³

et al. 2020

J Thorac Dis

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Backgrounds: Conventional ultrasound manual scanning and artificial diagnosis approaches in breast are considered to be operator-dependence, slight slow and error-prone. In this study, we used Automated Breast Ultrasound (ABUS) machine for the scanning, and deep convolutional neural network (CNN) technology, a kind of Deep Learning (DL) algorithm, for the detection and classification of breast nodules, aiming to achieve the automatic and accurate diagnosis of breast nodules. Methods: Two hundred and ninety-three lesions from 194 patients with definite pathological diagnosis results (117 benign and 176 malignancy) were recruited as case group. Another 70 patients without breast diseases were enrolled as control group. All the breast scans were carried out by an ABUS machine and then randomly divided into training set, verification set and test set, with a proportion of 7:1:2. In the training set, we constructed a detection model by a three-dimensionally U-shaped convolutional neural network (3D U-Net) architecture for the purpose of segment the nodules from background breast images. Processes such as residual block, attention connections, and hard mining were used to optimize the model while strategies of random cropping, flipping and rotation for data augmentation. In the test phase, the current model was compared with those in previously reported studies. In the verification set, the detection effectiveness of detection model was evaluated. In the classification phase, multiple convolutional layers and fully-connected layers were applied to set up a classification model, aiming to identify whether the nodule was malignancy.Results: Our detection model yielded a sensitivity of 91% and 1.92 false positive subjects per automatically scanned imaging. The classification model achieved a sensitivity of 87.0%, a specificity of 88.0% and an accuracy of 87.5%.Conclusions: Deep CNN combined with ABUS maybe a promising tool for easy detection and accurate diagnosis of breast nodule.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Study on automatic detection and classification of breast nodule using deep convolutional neural network system

Wang¹,

Liu²,

Yuan³

et al. 2020

J Thorac Dis

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“…Kozegar et al (17) survey the method of different CADe systems for ABUS images and analysis the workflow and model of method. Ikedo et al (18) proposed a fully automated method for segmenting breast tumors.…”

Section: Why Choose Abus?mentioning

confidence: 99%

Fully automated lesion segmentation and visualization in automated whole breast ultrasound (ABUS) images

Lee

Chang

Chou

et al. 2020

Quant Imaging Med Surg

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Background: The number of breast cancer patients has increased each year, and the demand for breast cancer detection has become quite large. There are many common breast cancer diagnostic tools. The latest automated whole breast ultrasound (ABUS) technology can obtain a complete breast tissue structure, which improves breast cancer detection technology. However, due to the large amount of ABUS image data, manual interpretation is time-consuming and labor-intensive. If there are lesions in multiple images, there may be some omissions. In addition, if further volume information or the three-dimensional shape of the lesion is needed for therapy, it is necessary to manually segment each lesion, which is inefficient for diagnosis.Therefore, automatic lesion segmentation for ABUS is an important issue for guiding therapy.Methods: Due to the amount of speckle noise in an ultrasonic image and the low contrast of the lesion boundary, it is quite difficult to automatically segment the lesion. To address the above challenges, this study proposes an automated lesion segmentation algorithm. The architecture of the proposed algorithm can be divided into four parts: (I) volume of interest selection, (II) preprocessing, (III) segmentation, and (IV) visualization. A volume of interest (VOI) is automatically selected first via a three-dimensional level-set, and then the method uses anisotropic diffusion to address the speckled noise and intensity inhomogeneity correction to eliminate shadowing artifacts before the adaptive distance regularization level set method (DRLSE) conducts segmentation. Finally, the two-dimensional segmented images are reconstructed for visualization in the three-dimensional space.Results: The ground truth is delineated by two radiologists with more than 10 years of experience in breast sonography. In this study, three performance assessments are carried out to evaluate the effectiveness of the proposed algorithm. The first assessment is the similarity measurement. The second assessment is the comparison of the results of the proposed algorithm and the Chan-Vese level set method. The third assessment is the volume estimation of phantom cases. In this study, in the 2D validation of the first assessment, the area Dice similarity coefficients of the real cases named cases A, real cases B and phantoms are 0.84±0.02, 0.86±0.03 and 0.92±0.02, respectively. The overlap fraction (OF) and overlap value (OV) of the real cases A are 0.84±0.06 and 0.78±0.04, real case B are 0.91±0.04 and 0.82±0.05, respectively. The overlap fraction (OF) and overlap value (OV) of the phantoms are 0.95±0.02 and 0.92±0.03, respectively. In the 3D validation, the volume Dice similarity coefficients of the real cases A, real cases B and phantoms are 0.85±0.02, 0.89±0.04 and 0.94±0.02, respectively. The overlap fraction (OF) and overlap value (OV) of the real cases A are 0.82±0.06 and 0.79±0.04, real cases B are 0.92±0.04 and 0.85±0.07, respectively. The overlap fraction (OF) and overlap value (OV) of the phantoms are 0.95±0.01 and 0.93±0.0...

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“…Because of the standard imaging procedure, it is possible to perform temporal analysis on prior and current exams. Since the ABUS images are standardly defined, many researcher paid lots of attention on tumor classification (Tan et al 2012Liu et al 2014;Lo et al 2014;van Zelst et al 2017) and cancer detection Kozegar et al 2017;Drukker et al 2014;Van Zelst et al 2017;van Zelst et al 2018;Ehsan et al 2017;Kozegar et al 2019) using various techniques. Currently GE invenia ABUS system is FDA-cleared for screening purpose [58] while Siemens ABVS system is FDA-cleared for diagnosis purpose [59].…”

Section: Breast Cancermentioning

confidence: 99%

Ultrasound tissue classification: a review

et al. 2020

Self Cite

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Ultrasound imaging is the most widespread medical imaging modality for creating images of the human body in clinical practice. Tissue classification in ultrasound has been established as one of the most active research areas, driven by many important clinical applications. In this paper, we present a survey on ultrasound tissue classification, focusing on recent advances in this area. We start with a brief review on the main clinical applications. We then introduce the traditional approaches, where the existing research on feature extraction and classifier design are reviewed. As deep learning approaches becoming popular for medical image analysis, the recent deep learning methods for tissue classification are also introduced. We briefly discuss the FDA-cleared techniques being used clinically. We conclude with the discussion on the challenges and research focus in future.

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Computer aided detection in automated 3-D breast ultrasound images: a survey

Cited by 32 publications

References 44 publications

Study on automatic detection and classification of breast nodule using deep convolutional neural network system

Study on automatic detection and classification of breast nodule using deep convolutional neural network system

Fully automated lesion segmentation and visualization in automated whole breast ultrasound (ABUS) images

Ultrasound tissue classification: a review

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