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Lei B, Huang S, Li H, Li R, Bian C, Chou YH, Qin J, Zhou P, Gong X, Cheng JZ. Self-co-attention neural network for anatomy segmentation in whole breast ultrasound. Med Image Anal 2020; 64:101753. [DOI: 10.1016/j.media.2020.101753] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2020] [Revised: 05/27/2020] [Accepted: 06/06/2020] [Indexed: 11/25/2022]
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Eybposh MH, Turani Z, Mehregan D, Nasiriavanaki M. Cluster-based filtering framework for speckle reduction in OCT images. BIOMEDICAL OPTICS EXPRESS 2018; 9:6359-6373. [PMID: 31065434 PMCID: PMC6490990 DOI: 10.1364/boe.9.006359] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/06/2018] [Revised: 10/24/2018] [Accepted: 10/29/2018] [Indexed: 05/27/2023]
Abstract
Optical coherence tomography (OCT) has become a popular modality in the dermatology discipline due to its moderate resolution and penetration depth. OCT images, however, contain a grainy pattern called speckle. To date, a variety of filtering techniques have been introduced to reduce speckle in OCT images. However, further improvement is required to reduce edge smoothing and the deterioration of small structures in OCT images after despeckling. In this manuscript, we present a novel cluster-based speckle reduction framework (CSRF) that consists of a clustering method, followed by a despeckling method. Since edges are borders of two adjacent clusters, the proposed framework leaves the edges intact. Moreover, the multiplicative speckle noise could be modeled as additive noise in each cluster. To evaluate the performance of CSRF and demonstrate its generic nature, a clustering method, namely k-means (KM), and, two pixelwise despeckling algorithms, including Lee filter (LF) and adaptive Wiener filter (AWF), are used. The results indicate that CSRF significantly improves the performance of despeckling algorithms. These improvements are evaluated on healthy human skin images in vivo using two numerical assessment measures including signal-to-noise ratio (SNR), and structural similarity index (SSIM).
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Affiliation(s)
- M Hossein Eybposh
- Sharif University of Technology, Department of Electrical Engineering, Tehran, Iran
| | - Zahra Turani
- Sharif University of Technology, Department of Electrical Engineering, Tehran, Iran
- Wayne State University, School of Medicine, Department of Dermatology, Detroit, MI, USA
| | - Darius Mehregan
- Wayne State University, School of Medicine, Department of Dermatology, Detroit, MI, USA
| | - Mohammadreza Nasiriavanaki
- Wayne State University, School of Medicine, Department of Dermatology, Detroit, MI, USA
- Wayne State University, Department of Biomedical Engineering, Detroit, MI, USA
- Barbara Ann Karmanos Cancer Institute, Detroit, Michigan, USA
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Opportunistic Breast Density Assessment in Women Receiving Low-dose Chest Computed Tomography Screening. Acad Radiol 2016; 23:1154-61. [PMID: 27283069 DOI: 10.1016/j.acra.2016.05.003] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2016] [Revised: 05/16/2016] [Accepted: 05/17/2016] [Indexed: 12/20/2022]
Abstract
RATIONALE AND OBJECTIVES Low-dose chest computed tomography (LDCT), increasingly being used for screening of lung cancer, may also be used to measure breast density, which is proven as a risk factor for breast cancer. In this study, we developed a segmentation method to measure quantitative breast density on CT images and correlated with magnetic resonance density. MATERIALS AND METHODS Forty healthy women receiving both LDCT and breast magnetic resonance imaging (MRI) were studied. A semiautomatic method was applied to quantify the breast density on LDCT images. The intra- and interoperator reproducibility was evaluated. The volumetric density on MRI was obtained by using a well-established automatic template-based segmentation method. The breast volume (BV), fibroglandular tissue volume (FV), and percent breast density (PD) measured on LDCT and MRI were compared. RESULTS The measurements of BV, FV, and PD on LDCT images yield highly consistent results, with the intraclass correlation coefficient of 0.999 for BV, 0.977 for FV, and 0.966 for PD for intraoperator reproducibility, and intraclass correlation coefficient of 0.953 for BV, 0.974 for FV, and 0.973 for PD for interoperator reproducibility. The BV, FV, and PD measured on LDCT and MRI were well correlated (all r ≥ 0.90). Bland-Altman plots showed that a larger BV and FV were measured on LDCT than on MRI. CONCLUSIONS The preliminary results showed that quantitative breast density can be measured from LDCT, and that our segmentation method could yield a high reproducibility on the measured volume and PD. The results measured on LDCT and MRI were highly correlated. Our results showed that LDCT may provide valuable information about breast density for evaluating breast cancer risk.
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Chen JH, Chan S, Tang YT, Hon JS, Tseng PC, Cheriyan AT, Shah NR, Yeh DC, Lee SK, Chen WP, McLaren CE, Su MY. Impact of positional difference on the measurement of breast density using MRI. Med Phys 2016; 42:2268-75. [PMID: 25979021 DOI: 10.1118/1.4917083] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023] Open
Abstract
PURPOSE This study investigated the impact of arms/hands and body position on the measurement of breast density using MRI. METHODS Noncontrast-enhanced T1-weighted images were acquired from 32 healthy women. Each subject received four MR scans using different experimental settings, including a high resolution hands-up, a low resolution hands-up, a high resolution hands-down, and finally, another high resolution hands-up after repositioning. The breast segmentation was performed using a fully automatic chest template-based method. The breast volume (BV), fibroglandular tissue volume (FV), and percent density (PD) measured from the four MR scan settings were analyzed. RESULTS A high correlation of BV, FV, and PD between any pair of the four MR scans was noted (r > 0.98 for all). Using the generalized estimating equation method, a statistically significant difference in mean BV among four settings was noted (left breast, score test p = 0.0056; right breast, score test p = 0.0016), adjusted for age and body mass index. Despite differences in BV, there were no statistically significant differences in the mean PDs among the four settings (p > 0.10 for left and right breasts). Using Bland-Altman plots, the smallest mean difference/bias and standard deviations for BV, FV, and PD were noted when comparing hands-up high vs low resolution when the breast positions were exactly the same. CONCLUSIONS The authors' study showed that BV, FV, and PD measurements from MRI of different positions were highly correlated. BV may vary with positions but the measured PD did not differ significantly between positions. The study suggested that the percent density analyzed from MRI studies acquired using different arms/hands and body positions from multiple centers can be combined for analysis.
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Affiliation(s)
- Jeon-Hor Chen
- Tu & Yuen Center for Functional Onco-Imaging, Department of Radiological Sciences, University of California, Irvine, California 92697-5020 and Department of Radiology, E-Da Hospital and I-Shou University, Kaohsiung 82445, Taiwan
| | - Siwa Chan
- Department of Radiology, Taichung Veterans General Hospital, Taichung 40705, Taiwan
| | - Yi-Ting Tang
- Department of Radiological Technology, China Medical University, Taichung 40402, Taiwan
| | - Jia Shen Hon
- Department of Radiological Technology, China Medical University, Taichung 40402, Taiwan
| | - Po-Chuan Tseng
- Tu & Yuen Center for Functional Onco-Imaging, Department of Radiological Sciences, University of California, Irvine, California 92697-5020
| | - Angela T Cheriyan
- Tu & Yuen Center for Functional Onco-Imaging, Department of Radiological Sciences, University of California, Irvine, California 92697-5020
| | - Nikita Rakesh Shah
- Tu & Yuen Center for Functional Onco-Imaging, Department of Radiological Sciences, University of California, Irvine, California 92697-5020
| | - Dah-Cherng Yeh
- Department of Surgery, Taichung Veterans General Hospital, Taichung 40705, Taiwan
| | - San-Kan Lee
- Department of Radiology, Taichung Veterans General Hospital, Taichung 40705, Taiwan
| | - Wen-Pin Chen
- Department of Epidemiology, University of California, Irvine, California 92697-5020
| | - Christine E McLaren
- Department of Epidemiology, University of California, Irvine, California 92697-5020
| | - Min-Ying Su
- Tu & Yuen Center for Functional Onco-Imaging, Department of Radiological Sciences, University of California, Irvine, California 92697-5020
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Gu P, Lee WM, Roubidoux MA, Yuan J, Wang X, Carson PL. Automated 3D ultrasound image segmentation to aid breast cancer image interpretation. ULTRASONICS 2016; 65:51-8. [PMID: 26547117 PMCID: PMC4702489 DOI: 10.1016/j.ultras.2015.10.023] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/23/2015] [Revised: 10/20/2015] [Accepted: 10/23/2015] [Indexed: 05/18/2023]
Abstract
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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Affiliation(s)
- Peng Gu
- Department of Electronic Science and Engineering, Nanjing University, 210093, China
| | - Won-Mean Lee
- Department of Radiology, University of Michigan, 48109, USA
| | | | - Jie Yuan
- Department of Electronic Science and Engineering, Nanjing University, 210093, China.
| | - Xueding Wang
- Department of Radiology, University of Michigan, 48109, USA
| | - Paul L Carson
- Department of Radiology, University of Michigan, 48109, USA.
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Imaging Breast Density: Established and Emerging Modalities. Transl Oncol 2015; 8:435-45. [PMID: 26692524 PMCID: PMC4700291 DOI: 10.1016/j.tranon.2015.10.002] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2015] [Revised: 09/30/2015] [Accepted: 10/06/2015] [Indexed: 11/23/2022] Open
Abstract
Mammographic density has been proven as an independent risk factor for breast cancer. Women with dense breast tissue visible on a mammogram have a much higher cancer risk than women with little density. A great research effort has been devoted to incorporate breast density into risk prediction models to better estimate each individual’s cancer risk. In recent years, the passage of breast density notification legislation in many states in USA requires that every mammography report should provide information regarding the patient’s breast density. Accurate definition and measurement of breast density are thus important, which may allow all the potential clinical applications of breast density to be implemented. Because the two-dimensional mammography-based measurement is subject to tissue overlapping and thus not able to provide volumetric information, there is an urgent need to develop reliable quantitative measurements of breast density. Various new imaging technologies are being developed. Among these new modalities, volumetric mammographic density methods and three-dimensional magnetic resonance imaging are the most well studied. Besides, emerging modalities, including different x-ray–based, optical imaging, and ultrasound-based methods, have also been investigated. All these modalities may either overcome some fundamental problems related to mammographic density or provide additional density and/or compositional information. The present review article aimed to summarize the current established and emerging imaging techniques for the measurement of breast density and the evidence of the clinical use of these density methods from the literature.
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Moon WK, Lo CM, Chang JM, Bae MS, Kim WH, Huang CS, Chen JH, Kuo MH, Chang RF. Rapid breast density analysis of partial volumes of automated breast ultrasound images. ULTRASONIC IMAGING 2013; 35:333-343. [PMID: 24081729 DOI: 10.1177/0161734613505998] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
Rapid volume density analysis (RVDA) for automated breast ultrasound (ABUS) has been proposed as a more efficient method for estimating breast density. In the current experiment, ABUS images were obtained for 67 breasts from 40 patients. For each case, three rectangular volumes of interest (VOIs) were extracted, including the VOIs located at the 6 and 12 o'clock positions relative to the nipple in the anterior to posterior pass and the lateral position relative to the nipple in the lateral pass. The centers of these VOIs were defined to align with the center of nipple, and the depths reached the retromammary fat boundary. The fuzzy c-means classifier was applied to differentiate the fibroglandular and fat tissues to estimate the density. The classification results of the three VOIs were averaged to obtain the breast density. The density correlations between the RVDA and the ABUS methods were 0.98 and 0.96 using Pearson's correlation and linear regression coefficients, respectively. The average computation times for RVDA and ABUS were 4.2 and 17.8 seconds, respectively, using an Intel Core2 2.66 GHz computer with 3.25 GB memory. In conclusion, the RVDA method offers a quantitative and efficient breast density estimation for ABUS.
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Affiliation(s)
- Woo Kyung Moon
- 1Department of Radiology, Seoul National University Hospital, Seoul, Korea
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Tsai IC, Huang YL, Liu PT, Chen MC. Left ventricular myocardium segmentation on delayed phase of multi-detector row computed tomography. Int J Comput Assist Radiol Surg 2012; 7:737-51. [PMID: 22528059 DOI: 10.1007/s11548-012-0688-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2012] [Accepted: 03/30/2012] [Indexed: 11/29/2022]
Abstract
RATIONALE AND OBJECTIVES Advanced ischemic heart disease is usually accompanied by left ventricular (LV) myocardial volume loss and an abnormal enhancing pattern on delayed phase of multi-detector row computed tomography (MDCT). To assist radiologists and physicians in estimating the LV myocardial volume on delayed phase, this paper proposes an adaptive segmentation method for contouring the myocardial region in the delayed-phase MDCT and for computing the volume. MATERIALS AND METHODS The proposed method uses an anisotropic diffusion filter as a preprocessing procedure to enhance contrast and reduce specks in MDCT imaging. This work picks the middle of mid-ventricular level image slices as the lead slice. The proposed method develops two contouring modes to sketch the myocardium contour on the lead slice. By establishing the obtained contours as the initial contours, the region-growing method is employed to identify the contour of the myocardial region for each slice. The convex-hull finding algorithm is then used to refine the extracted contour. Finally, the width properties of the myocardial region and the morphological operators are used to obtain the entire LV myocardial volume. RESULTS Twenty-seven healthy patients who had no symptoms of ischemic heart disease are examined to evaluate the performance of the proposed method. Compared with manual contours delineated by two experienced experts, the contouring results using computer simulation reveal that the proposed method reliably identifies contours similar to those obtained using manual sketching. CONCLUSION The proposed method provides robust contouring for the LV myocardium on delayed-phase MDCT. The potential role of this technique may substantially reduce the time required to sketch manually a precise contour with high stability.
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Affiliation(s)
- I-Chen Tsai
- Department of Radiology, Taichung Veterans General Hospital, Taichung, Taiwan
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Pistolese CA, Perretta T, Cossu E, Della Gatta F, Giura S, Simonetti G. Value of the correct diagnostic pathway through conventional imaging (mammography and ultrasound) in evaluating breast disease. Radiol Med 2011; 116:584-94. [PMID: 21431300 DOI: 10.1007/s11547-011-0657-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
PURPOSE This study evaluated the role of the correct diagnostic pathway through conventional imaging in evaluating breast disease. MATERIALS AND METHODS Six hundred patients aged between 35 and 75 years were enrolled in the study. All patients underwent detailed history and clinical examination, ultrasound (US) and mammography. US scans were repeated after mammography. All suspicious lesions were studied by cytological and histological characterisation and magnetic resonance (MR) imaging. RESULTS The first US scan showed 147 solid lesions, 67 lesions characterised by posterior acoustic shadowing and 193 areas of heterogeneous echostructure. The second US scan, performed after mammography, confirmed 123/147 solid nodular lesions, 53/67 lesions characterised by posterior acoustic shadowing and 183/193 areas of heterogeneous echostructure; it also showed 13 nodular lesions not seen on the first scan and two cases of nodular lesions with irregular calcifications. CONCLUSIONS Our experience suggests that US not performed in conjunction with mammography gives rise to incorrect diagnostic interpretations (either false positive or false negative results). The detection rate of the US scan performed after mammography increases from 4.16% to 5.5%.
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Affiliation(s)
- C A Pistolese
- Dipartimento di Diagnostica per Immagini, Imaging Molecolare, Radiologia Interventistica e Terapia Radiante, Università degli studi di Roma Tor Vergata (PTV), Viale Oxford 81, 00133, Roma, Italy.
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Moon WK, Shen YW, Huang CS, Luo SC, Kuzucan A, Chen JH, Chang RF. Comparative study of density analysis using automated whole breast ultrasound and MRI. Med Phys 2011; 38:382-9. [PMID: 21361206 DOI: 10.1118/1.3523617] [Citation(s) in RCA: 34] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
PURPOSE The purpose of this study is to compare the measurements of breast density using three-dimensional (3-D) automated whole breast ultrasound (ABUS) and magnetic resonance imaging (MRI). METHODS In this study, 3-D ABUS and MRI breast images were obtained from 40 patients-bilaterally in 27 patients and unilaterally (due to operation in the contralateral breast) in 13 patients, To differentiate the fibroglandular and fatty tissues in ABUS and MRI images, the fuzzy C-mean classifier was used. Calculated values for percent density and breast volume from the two modalities were compared to and correlated with linear regression analysis. Intraoperator and interoperator variations among eight cases were evaluated to verify the consistency of the density analysis. RESULTS Mean percent density and breast volume derived from ABUS (17.63 +/- 11.87% and 418.30 +/- 132.97 cm3, respectively) and MRI images (23.79 +/- 16.62% and 544.90 +/- 207.41 cm3) demonstrated good correlation (R = 0.917 and R = 0.884). Intraoperator and interoperator analyses yielded slightly larger coefficients of variation for percent density and breast volume in ABUS compared to MRI. However, the differences were not statistically significant. CONCLUSIONS ABUS and MRI showed high correlation for breast density and breast volume quantification. Both modalities could provide useful breast density information to physicians.
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Affiliation(s)
- Woo Kyung Moon
- Department of Radiology, Seoul National University Hospital, Seoul 110-744, Korea
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