Automated breast ultrasound (ABUS) as a screening tool: initial experience

Mostafa, Amera Abd Elsalam; Eltomey, Mohamed Adel; Elaggan, Ashraf; Hashish, Amel A.

doi:10.1186/s43055-019-0032-9

Cited by 7 publications

(9 citation statements)

References 16 publications

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“…BUS has the potential to be utilized as mammography’s adjunct [ 5 ]. A BUS’s improvement is the automated BUS ( ABUS ), having the ability in decreasing operator’s dependence when compared to conventional handheld ultrasound scans [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

Automatic semantic segmentation of breast tumors in ultrasound images based on combining fuzzy logic and deep learning—A feasibility study

et al. 2021

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Computer aided diagnosis (CAD) of biomedical images assists physicians for a fast facilitated tissue characterization. A scheme based on combining fuzzy logic (FL) and deep learning (DL) for automatic semantic segmentation (SS) of tumors in breast ultrasound (BUS) images is proposed. The proposed scheme consists of two steps: the first is a FL based preprocessing, and the second is a Convolutional neural network (CNN) based SS. Eight well-known CNN based SS models have been utilized in the study. Studying the scheme was by a dataset of 400 cancerous BUS images and their corresponding 400 ground truth images. SS process has been applied in two modes: batch and one by one image processing. Three quantitative performance evaluation metrics have been utilized: global accuracy (GA), mean Jaccard Index (mean intersection over union (IoU)), and mean BF (Boundary F1) Score. In the batch processing mode: quantitative metrics’ average results over the eight utilized CNNs based SS models over the 400 cancerous BUS images were: 95.45% GA instead of 86.08% without applying fuzzy preprocessing step, 78.70% mean IoU instead of 49.61%, and 68.08% mean BF score instead of 42.63%. Moreover, the resulted segmented images could show tumors’ regions more accurate than with only CNN based SS. While, in one by one image processing mode: there has been no enhancement neither qualitatively nor quantitatively. So, only when a batch processing is needed, utilizing the proposed scheme may be helpful in enhancing automatic ss of tumors in BUS images. Otherwise applying the proposed approach on a one-by-one image mode will disrupt segmentation’s efficiency. The proposed batch processing scheme may be generalized for an enhanced CNN based SS of a targeted region of interest (ROI) in any batch of digital images. A modified small dataset is available: https://www.kaggle.com/mohammedtgadallah/mt-small-dataset (S1 Data).

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Section: Introductionmentioning

confidence: 99%

Automatic semantic segmentation of breast tumors in ultrasound images based on combining fuzzy logic and deep learning—A feasibility study

et al. 2021

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“…Early detection of breast cancer aims to reduce morbidity and mortality rates. Mammography has been established as the imaging modality for screening and early detection of breast cancer; however, it is accused of having low sensitivity and specificity in women with dense breasts [1].…”

Section: Introductionmentioning

confidence: 99%

Can contrast-enhanced mammography replace dynamic contrast-enhanced MRI in the assessment of sonomammographic indeterminate breast lesions?

Kamal

Hanafy

Mansour

et al. 2020

Egypt J Radiol Nucl Med

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Background: Dynamic contrast-enhanced MRI of the breast has been used for several years in the assessment of indeterminate mammographic findings. Contrast-enhanced mammography is a relatively novel imaging technique that has shown comparable sensitivity and specificity to MRI. Contrast-enhanced mammography is a relatively easy feasible study with high sensitivity and low cost. Our aim was to assess the feasibility of replacing dynamic contrastenhanced (DCE)-MRI by contrast-enhanced mammography in the assessment of sonomammographic indeterminate lesions (BIRADS 3 and 4). Results: The study included 82 patients with 171 breast lesions. They all performed contrast-enhanced mammography and dynamic contrast-enhanced MRI. DCE-MRI sensitivity and NPV were significantly higher than those of contrast-enhanced mammogram (CEM). The overall accuracy of MRI was better than that of CEM; however, no statistically significant difference could be detected. Conclusion: Contrast-enhanced mammography and dynamic contrast-enhanced MRI improved the characterization of breast lesions. CEM showed slightly lower sensitivity and accuracy compared to MRI; however, because of being relatively easy, available, cheap, and acceptable by women, CEM can replace DC-MRI as a problem-solving tool in the characterization of indeterminate breast lesions.

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“…Automated breast ultrasound (ABUS) may be used instead of the more commonly applied hand-held ultrasound (HHUS) allowing a standardization of the image acquisition, complete coverage of the whole breast volume, and the delegation of the acquisition task to the technician [ 16 , 17 ]. In first clinical studies, ABUS demonstrated similar accuracy as hand-held ultrasound; moreover, ABUS increased the cancer detection rate in combination with conventional mammography [ 18 , 19 ]. One of the disadvantages of ABUS examinations is the large number of images resulting in an increased reading time for inexperienced radiologists.…”

Section: Introductionmentioning

confidence: 99%

Fully automatic classification of automated breast ultrasound (ABUS) imaging according to BI-RADS using a deep convolutional neural network

et al. 2022

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Purpose The aim of this study was to develop and test a post-processing technique for detection and classification of lesions according to the BI-RADS atlas in automated breast ultrasound (ABUS) based on deep convolutional neural networks (dCNNs). Methods and materials In this retrospective study, 645 ABUS datasets from 113 patients were included; 55 patients had lesions classified as high malignancy probability. Lesions were categorized in BI-RADS 2 (no suspicion of malignancy), BI-RADS 3 (probability of malignancy < 3%), and BI-RADS 4/5 (probability of malignancy > 3%). A deep convolutional neural network was trained after data augmentation with images of lesions and normal breast tissue, and a sliding-window approach for lesion detection was implemented. The algorithm was applied to a test dataset containing 128 images and performance was compared with readings of 2 experienced radiologists. Results Results of calculations performed on single images showed accuracy of 79.7% and AUC of 0.91 [95% CI: 0.85–0.96] in categorization according to BI-RADS. Moderate agreement between dCNN and ground truth has been achieved (κ: 0.57 [95% CI: 0.50–0.64]) what is comparable with human readers. Analysis of whole dataset improved categorization accuracy to 90.9% and AUC of 0.91 [95% CI: 0.77–1.00], while achieving almost perfect agreement with ground truth (κ: 0.82 [95% CI: 0.69–0.95]), performing on par with human readers. Furthermore, the object localization technique allowed the detection of lesion position slice-wise. Conclusions Our results show that a dCNN can be trained to detect and distinguish lesions in ABUS according to the BI-RADS classification with similar accuracy as experienced radiologists. Key Points • A deep convolutional neural network (dCNN) was trained for classification of ABUS lesions according to the BI-RADS atlas. • A sliding-window approach allows accurate automatic detection and classification of lesions in ABUS examinations.

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Automated breast ultrasound (ABUS) as a screening tool: initial experience

Cited by 7 publications

References 16 publications

Automatic semantic segmentation of breast tumors in ultrasound images based on combining fuzzy logic and deep learning—A feasibility study

Automatic semantic segmentation of breast tumors in ultrasound images based on combining fuzzy logic and deep learning—A feasibility study

Can contrast-enhanced mammography replace dynamic contrast-enhanced MRI in the assessment of sonomammographic indeterminate breast lesions?

Fully automatic classification of automated breast ultrasound (ABUS) imaging according to BI-RADS using a deep convolutional neural network

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