Fully Automated Nipple Detection in 3D Breast Ultrasound Images

Wang, Lei; Böhler, Tobias; Zöhrer, Fabian; Georgii, Joachim; Rauh, Claudia; Fasching, Peter A.; Brehm, B.; Schulz‐Wendtland, Rüdiger; Beckmann, Matthias W.; Uder, Michael; Hahn, Horst K.

doi:10.1007/978-3-319-07887-8_10

Cited by 3 publications

(3 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Several studies have reported that computer-assisted detection (CAD) software may enhance the diagnostic performance of ABUS [19][20][21][22]. Improvement in the performance of CAD software has been observed with advances in machine learning algorithms [19].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Localization and segmentation of Nipple Shadow Region (NSA) is an important process since (a) by determining the location, the nipple region serves as a reference point to understand the growth characteristics of a suspected lesion during its examination over a period of time, and (b) it denotes a point or a region that can be documented across different modalities (MRI, CT, X-ray) to refer to a specific lesion, thus facilitating the radiologist in determining its malignancy. 11 The increase in the false positive rate in the localization of NSA is the major challenge in improving the sensitivity of the nipple detection in the BUS images. To tackle this challenge, many Computer-Aided Detection (CADe) tools are employed.…”

Section: Introductionmentioning

confidence: 99%

“…This method requires all the slices of the 3D scan in order to detect the NSA in the form of a tube-like structure. 11 Chae et al, proposed a method for segmenting the nipple in Digital Breast Tomosynthesis (DBT) images using the shape and location of the nipple on the breast boundary. This method takes advantage of the protruding nature of the nipple and uses a square mask to detect the position of the visible nipple.…”

Section: Introductionmentioning

confidence: 99%

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

Raj

Nersisson

Mahesh³

et al. 2020

Ultrason Imaging

View full text Add to dashboard Cite

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.

show abstract

A hybrid method towards automated nipple detection in 3D breast ultrasound images

Wang

Böhler

Zöhrer

et al. 2014

2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

View full text Add to dashboard Cite

In clinical work-up of breast cancer, nipple position is an important marker to locate lesions. Moreover, it serves as an effective landmark to register a 3D automated breast ultrasound (ABUS) images to other imaging modalities, e.g., X-ray mammography, tomosynthesis or magnetic resonance imaging (MRI). However, the presence of speckle noises caused by the interference waves and variant imaging directions poses challenges to automatically identify nipple positions. In this work, a hybrid fully automatic method to detect nipple positions in ABUS images is presented. The method extends the multi-scale Laplacian-based method that we proposed previously, by integrating a specially designed Hessian-based method to locate the shadow area beneath the nipple and areola. Subsequently, the likelihood maps of nipple positions generated by both methods are combined to build a joint-likelihood map, where the final nipple position is extracted. To validate the efficiency and robustness, the extended hybrid method was tested on 926 ABUS images, resulting in a distance error of 7.08±10.96 mm (mean±standard deviation).

show abstract

Fully Automated Nipple Detection in 3D Breast Ultrasound Images

Cited by 3 publications

References 5 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

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

A hybrid method towards automated nipple detection in 3D breast ultrasound images

Contact Info

Product

Resources

About