A computerized volumetric segmentation method applicable to multi-centre MRI data to support computer-aided breast tissue analysis, density assessment and lesion localization

Ertas, Gokhan; Doran, Simon J.; Leach, Martin O.

doi:10.1007/s11517-016-1484-y

Cited by 22 publications

(16 citation statements)

References 44 publications

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“…To reduce these over‐ and undersegmentations, 3D morphological image opening is performed, followed by closing using two cylindrical structuring elements having the same radius of 3 voxels but different heights of 3 voxels and 25 voxels in the axial direction. These parameters were found by experimentation during our previous study …”

Section: Methodsmentioning

confidence: 85%

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Breast MRI segmentation for density estimation: Do different methods give the same results and how much do differences matter?

Doran

Hipwell²,

Denholm

et al. 2017

Medical Physics

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PurposeTo compare two methods of automatic breast segmentation with each other and with manual segmentation in a large subject cohort. To discuss the factors involved in selecting the most appropriate algorithm for automatic segmentation and, in particular, to investigate the appropriateness of overlap measures (e.g., Dice and Jaccard coefficients) as the primary determinant in algorithm selection.MethodsTwo methods of breast segmentation were applied to the task of calculating MRI breast density in 200 subjects drawn from the Avon Longitudinal Study of Parents and Children, a large cohort study with an MRI component. A semiautomated, bias‐corrected, fuzzy C‐means (BC‐FCM) method was combined with morphological operations to segment the overall breast volume from in‐phase Dixon images. The method makes use of novel, problem‐specific insights. The resulting segmentation mask was then applied to the corresponding Dixon water and fat images, which were combined to give Dixon MRI density values. Contemporaneously acquired T1‐ and T2‐weighted image datasets were analyzed using a novel and fully automated algorithm involving image filtering, landmark identification, and explicit location of the pectoral muscle boundary. Within the region found, fat‐water discrimination was performed using an Expectation Maximization–Markov Random Field technique, yielding a second independent estimate of MRI density.ResultsImages are presented for two individual women, demonstrating how the difficulty of the problem is highly subject‐specific. Dice and Jaccard coefficients comparing the semiautomated BC‐FCM method, operating on Dixon source data, with expert manual segmentation are presented. The corresponding results for the method based on T1‐ and T2‐weighted data are slightly lower in the individual cases shown, but scatter plots and interclass correlations for the cohort as a whole show that both methods do an excellent job in segmenting and classifying breast tissue.ConclusionsEpidemiological results demonstrate that both methods of automated segmentation are suitable for the chosen application and that it is important to consider a range of factors when choosing a segmentation algorithm, rather than focus narrowly on a single metric such as the Dice coefficient.

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Section: Methodsmentioning

confidence: 85%

“…The BC‐FCM variant we implemented is described in . Formally, the algorithm does not require a training dataset and so is an unsupervised clustering algorithm.…”

Section: Methodsmentioning

confidence: 99%

Breast MRI segmentation for density estimation: Do different methods give the same results and how much do differences matter?

Doran

Hipwell²,

Denholm

et al. 2017

Medical Physics

Self Cite

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“…Moreover, with the recent development of radiomics and radiogenomics in breast cancer [35–39], we expect this proposed method would facilitate such analysis of BUS images and generate interesting findings. The limit of the proposed scheme is that the largest region criterion in contour initialization could reduce the efficiency of the method in cases with multiple tumors, because the detection of a second mass is deliberately ignored.…”

Section: Discussionmentioning

confidence: 99%

Automated breast tumor detection and segmentation with a novel computational framework of whole ultrasound images

Liu

Qin

et al. 2018

Med Biol Eng Comput

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Due to the low contrast and ambiguous boundaries of the tumors in breast ultrasound (BUS) images, it is still a challenging task to automatically segment the breast tumors from the ultrasound. In this paper, we proposed a novel computational framework that can detect and segment breast lesions fully automatic in the whole ultrasound images. This framework includes several key components: preprocessing, contour initialization, and tumor segmentation. In the pre-processing step, we applied non-local low-rank (NLLR) filter to reduce the speckle noise. In contour initialization step, we cascaded a two-step Otsu-based adaptive thresholding (OBAT) algorithm with morphologic operations to effectively locate the tumor regions and initialize the tumor contours. Finally, given the initial tumor contours, the improved Chan-Vese model based on the ratio of exponentially weighted averages (CV-ROEWA) method was utilized. This pipeline was tested on a set of 61 breast ultrasound (BUS) images with diagnosed tumors. The experimental results in clinical ultrasound images prove the high accuracy and robustness of the proposed framework, indicating its potential applications in clinical practice.

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“…In accord with recent studies of MRI data segmentation between the breast and the pectoral muscle, the anterior extent of Ella’s chest wall remained flat and aligned on the coronal plane. [34, 35] To position the breast phantoms on the Ella model before the fusing operation detailed below, two voxels were manually selected for centering the posterior coronal layer of all left and right breast phantoms. These locations were determined by orienting the base of each breast phantom on the inmost posterior coronal plane in Ella’s anterior thorax that solely overlapped breast tissue, fat, and skin, i.e., without encroaching on the pectoral fascia or adjacent muscle tissue.…”

Section: Methodsmentioning

confidence: 99%

Automated modification and fusion of voxel models to construct body phantoms with heterogeneous breast tissue: Application to MRI simulations

Rispoli

Wright

Malloy

et al. 2017

JBGC

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Background Human voxel models incorporating detailed anatomical features are vital tools for the computational evaluation of electromagnetic (EM) fields within the body. Besides whole-body human voxel models, phantoms representing smaller heterogeneous anatomical features are often employed; for example, localized breast voxel models incorporating fatty and fibroglandular tissues have been developed for a variety of EM applications including mammography simulation and dosimetry, magnetic resonance imaging (MRI), and ultra-wideband microwave imaging. However, considering wavelength effects, electromagnetic modeling of the breast at sub-microwave frequencies necessitates detailed breast phantoms in conjunction with whole-body voxel models. Methods Heterogeneous breast phantoms are sized to fit within radiofrequency coil hardware, modified by voxel-wise extrusion, and fused to whole-body models using voxel-wise, tissue-dependent logical operators. To illustrate the utility of this method, finite-difference time-domain simulations are performed using a whole-body model integrated with a variety of available breast phantoms spanning the standard four tissue density classifications representing the majority of the population. Results The software library uses a combination of voxel operations to seamlessly size, modify, and fuse eleven breast phantoms to whole-body voxel models. The software is publicly available on GitHub and is linked to the file exchange at MATLAB® Central. Simulations confirm the proportions of fatty and fibroglandular tissues in breast phantoms have significant yet predictable implications on projected power deposition in tissue. Conclusions Breast phantoms may be modified and fused to whole-body voxel models using the software presented in this work; user considerations for the open-source software and resultant phantoms are discussed. Furthermore, results indicate simulating breast models as predominantly fatty tissue can considerably underestimate the potential for tissue heating in women with substantial fibroglandular tissue.

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A computerized volumetric segmentation method applicable to multi-centre MRI data to support computer-aided breast tissue analysis, density assessment and lesion localization

Cited by 22 publications

References 44 publications

Breast MRI segmentation for density estimation: Do different methods give the same results and how much do differences matter?

Breast MRI segmentation for density estimation: Do different methods give the same results and how much do differences matter?

Automated breast tumor detection and segmentation with a novel computational framework of whole ultrasound images

Automated modification and fusion of voxel models to construct body phantoms with heterogeneous breast tissue: Application to MRI simulations

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