A fully automatic algorithm for segmentation of the breasts in DCE-MR images.

Valentina Giannini, Anna Vignati, Lia Morra, Diego Persano, Davide Brizzi, Luca Carbonaro, Alberto Bert, Francesco Sardanelli, Daniele Regge

Research output: Contribution to journalArticle

Abstract

Automatic segmentation of the breast and axillary region is an important preprocessing step for automatic lesion detection in breast MR and dynamic contrast-enhanced-MR studies. In this paper, we present a fully automatic procedure based on the detection of the upper border of the pectoral muscle. Compared with previous methods based on thresholding, this method is more robust to noise and field inhomogeneities. The method was quantitatively evaluated on 31 cases acquired from two centers by comparing the results with a manual segmentation. Results indicate good overall agreement within the reference segmentation (overlap=0.79 ± 0.09, recall=0.95 ± 0.02, precision=0.82 ± 0.1).

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Muscle
Breast
Pectoralis Muscles
Noise

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Biomedical Engineering
  • Health Informatics

Cite this

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title = "A fully automatic algorithm for segmentation of the breasts in DCE-MR images.",
abstract = "Automatic segmentation of the breast and axillary region is an important preprocessing step for automatic lesion detection in breast MR and dynamic contrast-enhanced-MR studies. In this paper, we present a fully automatic procedure based on the detection of the upper border of the pectoral muscle. Compared with previous methods based on thresholding, this method is more robust to noise and field inhomogeneities. The method was quantitatively evaluated on 31 cases acquired from two centers by comparing the results with a manual segmentation. Results indicate good overall agreement within the reference segmentation (overlap=0.79 ± 0.09, recall=0.95 ± 0.02, precision=0.82 ± 0.1).",
author = "Valentina Giannini and Anna Vignati and Lia Morra and Diego Persano and Davide Brizzi and Luca Carbonaro and Alberto Bert and Francesco Sardanelli and Daniele Regge",
year = "2010",
language = "English",
pages = "3146--3149",
journal = "Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference",
issn = "1557-170X",
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T1 - A fully automatic algorithm for segmentation of the breasts in DCE-MR images.

AU - Giannini, Valentina

AU - Vignati, Anna

AU - Morra, Lia

AU - Persano, Diego

AU - Brizzi, Davide

AU - Carbonaro, Luca

AU - Bert, Alberto

AU - Sardanelli, Francesco

AU - Regge, Daniele

PY - 2010

Y1 - 2010

N2 - Automatic segmentation of the breast and axillary region is an important preprocessing step for automatic lesion detection in breast MR and dynamic contrast-enhanced-MR studies. In this paper, we present a fully automatic procedure based on the detection of the upper border of the pectoral muscle. Compared with previous methods based on thresholding, this method is more robust to noise and field inhomogeneities. The method was quantitatively evaluated on 31 cases acquired from two centers by comparing the results with a manual segmentation. Results indicate good overall agreement within the reference segmentation (overlap=0.79 ± 0.09, recall=0.95 ± 0.02, precision=0.82 ± 0.1).

AB - Automatic segmentation of the breast and axillary region is an important preprocessing step for automatic lesion detection in breast MR and dynamic contrast-enhanced-MR studies. In this paper, we present a fully automatic procedure based on the detection of the upper border of the pectoral muscle. Compared with previous methods based on thresholding, this method is more robust to noise and field inhomogeneities. The method was quantitatively evaluated on 31 cases acquired from two centers by comparing the results with a manual segmentation. Results indicate good overall agreement within the reference segmentation (overlap=0.79 ± 0.09, recall=0.95 ± 0.02, precision=0.82 ± 0.1).

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