Automatic atlas‐based segmentation of the breast in MRI for 3D breast volume computation

Ortíz, Carolina; Martel, Anne L.

doi:10.1118/1.4748504

Cited by 44 publications

(38 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some model‐based segmentation methods were also developed. However, the training of a sophisticated model required establishing atlases in advance by manual operation of experts, which was tedious and time‐consuming …”

Section: Introductionmentioning

confidence: 99%

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

Jiang

Xiao

et al. 2017

Med. Phys.

View full text Add to dashboard Cite

show abstract

Section: Introductionmentioning

confidence: 99%

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

Jiang

Xiao

et al. 2017

Med. Phys.

View full text Add to dashboard Cite

show abstract

“…Fat-suppressed sequences will lead to difficulties for many of the algorithms so far presented in the literature, as there will be no strong boundary between the fat of the breast and the chest wall, with both appearing hypo-intense on images. Several of the larger studies [11, 18, 12] used research data acquired with a Dixon sequence from a non-clinical cohort [4] and our own work draws its data from a UK’s MARIBS screening trial [26]. An important endpoint in both of these trials was the production of breast density data, for which accurate segmentation is important.…”

Section: Discussionmentioning

confidence: 99%

“…Gallego-Ortiz and Martel [11] demonstrated an alternative atlas-based approach, incorporating entropy-based groupwise registration, maximal phase-congruency and Laplacian mapping. They applied their work to a large image cohort scanned with a Dixon MR imaging sequence.…”

Section: Introductionmentioning

confidence: 99%

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

Ertas

Doran

Leach

2016

Med Biol Eng Comput

View full text Add to dashboard Cite

Density assessment and lesion localization in breast MRI require accurate segmentation of breast tissues. A fast, computerized algorithm for volumetric breast segmentation, suitable for multi-centre data, has been developed, employing 3D bias-corrected fuzzy c-means clustering and morphological operations. The full breast extent is determined on T1-weighted images without prior information concerning breast anatomy. Left and right breasts are identified separately using automatic detection of the midsternum. Statistical analysis of breast volumes from eighty-two women scanned in a UK multi-centre study of MRI screening shows that the segmentation algorithm performs well when compared with manually corrected segmentation, with high relative overlap (RO), high true-positive volume fraction (TPVF) and low false-positive volume fraction (FPVF), and has an overall performance of RO 0.94 ± 0.05, TPVF 0.97 ± 0.03 and FPVF 0.04 ± 0.06, respectively (training: 0.93 ± 0.05, 0.97 ± 0.03 and 0.04 ± 0.06; test: 0.94 ± 0.05, 0.98 ± 0.02 and 0.05 ± 0.07).

show abstract

“…Their automatic algorithms are useful, but their method is validated by limited cases. Ortiz et al [10] present an automatic atlas-based method to segment the breast region in MRI data based on phase congruency filters and Poisson surface reconstruction algorithm. They need prior information about anatomy to construct the atlas set.…”

Section: Introductionmentioning

confidence: 99%

Automatic segmentation of breast and fibroglandular tissue in breast MRI using local adaptive thresholding

Fooladivanda

Shokouhi

Ahmadinejad

et al. 2014

2014 21th Iranian Conference on Biomedical Engineering (ICBME)

View full text Add to dashboard Cite

Breast density is considered as an important risk factor associated with the development of breast cancer. Breast and fibroglandular tissue segmentation is the main step to compute breast density in Magnetic Resonance Imaging (MRI). This study presents an automatic algorithm to segment breast and fibroglandular tissue in MRI. It is a difficult task due to bias field and similar signal intensity between fibroglandular tissue and pectoral muscle. Our proposed segmentation approach has been developed based on the local adaptive thresholding to dominate on intensity inhomogeneity due to bias field and the low contrast intensity of the boundary between breast and pectoral muscle. The presented approach is validated with a dataset of 2520 bilateral axial breast MR images from 45 women that include all of Breast Imaging Reporting and Data System (BI-RADS) breast density range. Five quantitative metrics as DiceSimilarity Coefficient (DSC), Jaccard Coefficient (JC), total overlap, False Negative (FN) and False Positive (FP) are employed to compare similarity between manual and automatic segmentations. For breast segmentation, the presented approach achieves DSC, JC, total overlap, FN and FP values of 0.90, 0.82, 0.89, 0.1 and 0.09, respectively. For fibroglandular tissue segmentation, we attain DSC, JC, total overlap, FN and FP values of 0.96, 0.94, 0.98, 0.02 and 0.04, respectively.

show abstract

Automatic atlas‐based segmentation of the breast in MRI for 3D breast volume computation

Cited by 44 publications

References 25 publications

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

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

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

Automatic segmentation of breast and fibroglandular tissue in breast MRI using local adaptive thresholding

Contact Info

Product

Resources

About