Lesion Segmentation in Automated 3D Breast Ultrasound: Volumetric Analysis

Agarwal, Richa; Díaz, Oliver; Lladó, Xavier; Gubern-Mérida, Albert; Vilanova, Joan C.; Martí, Robert

doi:10.1177/0161734617737733

Cited by 21 publications

(10 citation statements)

References 34 publications

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“…Further improvement in the performance of the CAD software might be achieved by modifying it to focus on the challenging factors revealed by our study (i.e., factors associated with false-negative outcomes). Promising results have been reported in recent studies of CAD applications using the latest deep learning algorithms [32,33].…”

Section: Discussionmentioning

confidence: 98%

False-negative results on computer-aided detection software in preoperative automated breast ultrasonography of breast cancer patients

et al. 2021

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Epub ahead of printPurpose: The purpose of this study was to measure the cancer detection rate of computer-aided detection (CAD) software in preoperative automated breast ultrasonography (ABUS) of breast cancer patients and to determine the characteristics associated with false-negative outcomes. Methods: A total of 129 index lesions (median size, 1.7 cm; interquartile range, 1.2 to 2.4 cm) from 129 consecutive patients (mean age±standard deviation, 53.4±11.8 years) who underwent preoperative ABUS from December 2017 to February 2018 were assessed. An index lesion was defined as a breast cancer confirmed by ultrasonography (US)-guided core needle biopsy. The detection rate of the index lesions, positive predictive value (PPV), and false-positive rate (FPR) of the CAD software were measured. Subgroup analysis was performed to identify clinical and US findings associated with false-negative outcomes. Results: The detection rate of the CAD software was 0.84 (109 of 129; 95% confidence interval, 0.77 to 0.90). The PPV and FPR were 0.41 (221 of 544; 95% CI, 0.36 to 0.45) and 0.45 (174 of 387; 95% CI, 0.40 to 0.50), respectively. False-negative outcomes were more frequent in asymptomatic patients (P<0.001) and were associated with the following US findings: smaller size (P=0.001), depth in the posterior third (P=0.002), angular or indistinct margin (P<0.001), and absence of architectural distortion (P<0.001). Conclusion: The CAD software showed a promising detection rate of breast cancer. However, radiologists should judge whether CAD software-marked lesions are true-or false-positive lesions, considering its low PPV and high FPR. Moreover, it would be helpful for radiologists to consider the characteristics associated with false-negative outcomes when reading ABUS with CAD.

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Section: Discussionmentioning

confidence: 98%

False-negative results on computer-aided detection software in preoperative automated breast ultrasonography of breast cancer patients

et al. 2021

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“…In another analysis of 142 biopsy-proven DCIS cases examining the use of ABUS in guiding breast conservation surgery, ABUS was superior to HHUS in both surgery planning and predicting recurrence, with a detection rate significantly higher (χ2 = 268.000, P < 0.001) [63]. Semiautomated and automated algorithms for ABUS lesion segmentation and volume measuring are under investigation [66,67].…”

Section: Symptomatic Breast Imagingmentioning

confidence: 99%

Automated Breast Ultrasound: Technical Aspects, Impact on Breast Screening, and Future Perspectives

Allajbeu

Hickman

Payne

et al. 2021

Curr Breast Cancer Rep

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Purpose of Review Automated breast ultrasound (ABUS) is a three-dimensional imaging technique, used as a supplemental screening tool in women with dense breasts. This review considers the technical aspects, pitfalls, and the use of ABUS in screening and clinical practice, together with new developments and future perspectives. Recent Findings ABUS has been approved in the USA and Europe as a screening tool for asymptomatic women with dense breasts in addition to mammography. Supplemental US screening has high sensitivity for cancer detection, especially early-stage invasive cancers, and reduces the frequency of interval cancers. ABUS has similar diagnostic performance to handheld ultrasound (HHUS) and is designed to overcome the drawbacks of operator dependence and poor reproducibility. Concerns with ABUS, like HHUS, include relatively high recall rates and lengthy reading time when compared to mammography. ABUS is a new technique with unique features; therefore, adequate training is required to improve detection and reduce false positives. Computer-aided detection may reduce reading times and improve cancer detection. Other potential applications of ABUS include local staging, treatment response evaluation, breast density assessment, and integration of radiomics. Summary ABUS provides an efficient, reproducible, and comprehensive supplemental imaging technique in breast screening. Developments with computer-aided detection may improve the sensitivity and specificity as well as radiologist confidence and reduce reading times, making this modality acceptable in large volume screening centers.

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“…Pons et al [18] reported that their evaluated automated method achieved a DSC of 0.49 using a Markov Random Field (MRF) and a Maximum a Posteriori (MAP) approach, by applying it to clinical data. Agarwal et al [19] developed a semi-automatic framework for breast lesion segmentation in ABUS volumes which is based on the Watershed algorithm. Rodrigues et al [20] took the advantage of pixel-wise classification and achieved a DSC of 0.824.…”

Section: Fig 1 a Malignant Lesion In Breast Ultrasoundmentioning

confidence: 99%

Lesion Segmentation in Ultrasound Using Semi-Pixel-Wise Cycle Generative Adversarial Nets

Xing

Wang

et al. 2021

IEEE/ACM Trans. Comput. Biol. and Bioinf.

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Breast cancer is the most common invasive cancer with the highest cancer occurrence in females. Handheld ultrasound is one of the most efficient ways to identify and diagnose the breast cancer. The area and the shape information of a lesion is very helpful for clinicians to make diagnostic decisions. In this study we propose a new deep-learning scheme, semi-pixel-wise cycle generative adversarial net (SPCGAN) for segmenting the lesion in 2D ultrasound. The method takes the advantage of a fully convolutional neural network (FCN) and a generative adversarial net to segment a lesion by using prior knowledge. We compared the proposed method to a fully connected neural network and the level set segmentation method on a test dataset consisting of 32 malignant lesions and 109 benign lesions. Our proposed method achieved a Dice similarity coefficient (DSC) of 0.92 while FCN and the level set achieved 0.90 and 0.79 respectively. Particularly, for malignant lesions, our method increases the DSC (0.90) of the fully connected neural network to 0.93 significantly (p <0.001). The results show that our SPCGAN can obtain robust segmentation results. The framework of SPCGAN is particularly effective when sufficient training samples are not available compared to FCN. Our proposed method may be used to relieve the radiologists' burden for annotation.

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Lesion Segmentation in Automated 3D Breast Ultrasound: Volumetric Analysis

Cited by 21 publications

References 34 publications

False-negative results on computer-aided detection software in preoperative automated breast ultrasonography of breast cancer patients

False-negative results on computer-aided detection software in preoperative automated breast ultrasonography of breast cancer patients

Automated Breast Ultrasound: Technical Aspects, Impact on Breast Screening, and Future Perspectives

Lesion Segmentation in Ultrasound Using Semi-Pixel-Wise Cycle Generative Adversarial Nets

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