Fully automated segmentation of whole breast using dynamic programming in dynamic contrast enhanced MR images

Jiang, Luan; Hu, Xiaoxin; Xiao, Qin; Gu, Yajia; Li, Qiang

doi:10.1002/mp.12254

Cited by 20 publications

(18 citation statements)

References 45 publications

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“…Wu et al [15], Lin et al [16], and Wang et al [17] proposed edge-based methods for detecting the breast-chest wall boundary by relying on the Canny edge detection method and a Hessian-based filter. Jiang et al [18] and Rosado-Toro et al [19] presented variants of the dynamic programming approach to segment the whole breast. All of the mentioned approaches relied on the visible contrast between the breast region and the chest wall and their processes are dependent on the presence of the fat along the anterior of the chest wall.…”

Section: Introductionmentioning

confidence: 99%

Breast-region segmentation in MRI using chest region atlas and SVM

Fooladivanda¹,

Shokouhi²,

Ahmadinejad³

2017

Turk J Elec Eng & Comp Sci

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An important step for computerized analysis of breast magnetic resonance imaging (MRI) is segmentation of the breast region. Due to the similar signal intensity of fibroglandular tissue and the chest wall, the segmentation process is difficult for breasts with fibroglandular tissue connected to the chest wall. In order to overcome this challenge, a new framework is presented that relies on a chest region atlas. The proposed method first detects the approximated breast-chest wall boundary using an intensity-based operation. A support vector machine (SVM) then determines the connectivity of fibroglandular tissue to the chest wall by the extracted features from the obtained breast-chest wall boundary. Finally, the obtained breast-chest wall boundary is accurately refined using the geometric shape of the chest region, which is obtained by an atlas-based segmentation method. The proposed method is validated using a dataset of 5964 breast MRI images from 126 women. The Dice similarity coefficient (DSC), total overlap (TO), false negative (FN), and false positive (FP) values are calculated to measure the similarity between automatic and manual segmentation results. Our method achieves DSC, TO, FN, and FP values of 96.46%, 96.41%, 3.59%, and 3.51%, respectively. The results prove the effectiveness of the presented algorithm for breasts with different sizes, shapes, and density patterns.

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Section: Introductionmentioning

confidence: 99%

Breast-region segmentation in MRI using chest region atlas and SVM

Fooladivanda¹,

Shokouhi²,

Ahmadinejad³

2017

Turk J Elec Eng & Comp Sci

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“…In breast CAD, breast segmentation is an important first pre-processing step to speed up the subsequent processes without losing any important anatomical information [8,9,10,11,12,13,14,15,16]. For example, breast segmentation (estimating the breast boundary and chest wall boundaries) removes unnecessary regions such as the heart, lung and liver.…”

Section: Introductionmentioning

confidence: 99%

Segmentation of breast MR images using a generalised 2D mathematical model with inflation and deflation forces of active contours

Rampun

Scotney

Morrow

et al. 2019

Artificial Intelligence in Medicine

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In medical computer aided diagnosis systems, image segmentation is one of the major pre-processing steps used to ensure only the region of interest, such as the breast region, will be processed in subsequent steps. Nevertheless, breast segmentation is a difficult task due to low contrast and inhomogeneity, especially when estimating the chest wall in magnetic resonance (MR) images. In fact, the chest wall comprises fat, skin, muscles, and the thoracic skeleton, which can misguide automatic methods when attempting to estimate its location. The objective of the study is to develop a fully automated method for breast and pectoral muscle boundary estimation in MR images. Firstly, we develop a 2D breast mathematical model based on 30 MRI slices (from a patient) and identify important landmarks to obtain a model for the general shape of the breast in an axial plane. Subsequently, we use Otsu's thresholding approach and Canny $ Fully documented templates are available in the elsarticle package on CTAN.

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“…The reported Dice similarity coefficient (DSC) of deep learning‐based methods to segment the chest wall in extremely dense breasts is 0.921 . The performance of knowledge‐based methods ranges from 0.944 to 0.96 . A direct comparison of the two approaches using a large MRI data set of ACR 4 breast would shed light on the advantages and pitfalls of both approaches.…”

Section: Introductionmentioning

confidence: 99%

“…These detailed methods can be divided into two groups, knowledge-based methods and deep learning-based methods. Knowledge-based methods use intensity operations and gradient signs, 15,16 edge properties 8,[17][18][19][20] , or a priori atlases. 9,10 Deep learning-based methods for chest wall segmentation have used artificial neural networks in the form of convolutional neural networks.…”

Section: Introductionmentioning

confidence: 99%

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Knowledge‐based and deep learning‐based automated chest wall segmentation in magnetic resonance images of extremely dense breasts

et al. 2019

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Purpose Segmentation of the chest wall, is an important component of methods for automated analysis of breast magnetic resonance imaging (MRI). Methods reported to date show promising results but have difficulties delineating the muscle border correctly in breasts with a large proportion of fibroglandular tissue (i.e., dense breasts). Knowledge‐based methods (KBMs) as well as methods based on deep learning have been proposed, but a systematic comparison of these approaches within one cohort of images is currently lacking. Therefore, we developed a KBM and a deep learning method for segmentation of the chest wall in MRI of dense breasts and compared their performances. Methods Two automated methods were developed, an optimized KBM incorporating heuristics aimed at shape, location, and gradient features, and a deep learning‐based method (DLM) using a dilated convolution neural network. A data set of 115 T1‐weighted MR images was randomly selected from MR images of women with extremely dense breasts (ACR BI‐RADS category 4) participating in a screening trial of women (mean age 56.6 yr, range 49.5–75.2 yr) with dense breasts. Manual segmentations of the chest wall, acquired under supervision of an experienced breast radiologist, were available for all data sets. Both methods were optimized using the same randomly selected 36 MRI data sets from a total of 115 data sets. Each MR data set consisted of 179 transversal images with voxel size 0.64 mm3 × 0.64 mm3 × 1.00 mm3. In the remaining 79 data sets, the results of both segmentation methods were qualitatively evaluated. A radiologist reviewed the segmentation results of both methods in all transversal images (n = 14 141) and determined whether the result would impact the ability to accurately determine the volume of fibroglandular and fatty tissue and whether segmentations masked breast regions that might harbor lesions. When no relevant deviation was detected, the result was considered successful. In addition, all segmentations were quantitatively assessed using the Dice similarity coefficient (DSC) and Hausdorff distance (HD), 95th percentile of the Hausdorff distance (HD95), false positive fraction (FPF), and false negative fraction (FNF) metrics. Results According to the radiologist's evaluation, the DLM had a significantly higher success rate than the KBM (81.6% vs 78.4%, P < 0.01). The success rate was further improved to 92.1% by combining both methods. Similarly, the DLM had significantly lower values for FNF (0.003 ± 0.003 vs 0.009 ± 0.011, P < 0.01) and HD95 (2.58 ± 1.78 mm vs 3.37 ± 2.11, P < 0.01). However, the KBM resulted in a significantly lower FPF than the DLM (0.018 ± 0.009 vs 0.030 ± 0.009, P < 0.01).There was no significant difference between the KBM and DLM in terms of DSC (0.982 ± 0.006 vs 0.984 ± 0.008, P = 0.08) or HD (24.14 ± 20.69 mm vs 12.81 ± 27.28 mm, P = 0.05). Conclusion Both optimized knowledge‐based and DLM showed good results to segment the pectoral muscle in women with dense breasts. Qualitatively assessed, the DLM was the most robust...

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Fully automated segmentation of whole breast using dynamic programming in dynamic contrast enhanced MR images

Abstract: Our fully automated method could robustly achieve high segmentation accuracy and efficiency. It would be useful for developing CAD systems for quantitative analysis of FGT and BPE in 3-D DCE-MRI.

Cited by 20 publications

References 45 publications

Breast-region segmentation in MRI using chest region atlas and SVM

Breast-region segmentation in MRI using chest region atlas and SVM

Segmentation of breast MR images using a generalised 2D mathematical model with inflation and deflation forces of active contours

Knowledge‐based and deep learning‐based automated chest wall segmentation in magnetic resonance images of extremely dense breasts

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