Abstract
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).
Highlights
Magnetic resonance imaging (MRI) is gaining increased acceptance for the diagnosis of breast cancer and the differential diagnosis of enhancing lesions [24], with enhancement information that reflects vascularity and permeability of breast tissues
Breast MRI offers the potential for accurate measurement of fibroglandular tissue volume to assess breast density, which is a strong risk factor associated with the development of breast cancer [44, 3, 19, 38]
Each position along the horizontal axis represents a threshold value ROthresh, and on the vertical axis, we plot the number of segmented breasts for which the calculated relative overlap (RO) value is greater than this threshold, expressed as a percentage of the total number of breasts
Summary
Magnetic resonance imaging (MRI) is gaining increased acceptance for the diagnosis of breast cancer and the differential diagnosis of enhancing lesions [24], with enhancement information that reflects vascularity and permeability of breast tissues. Breast MRI offers the potential for accurate measurement of fibroglandular tissue volume to assess breast density, which is a strong risk factor associated with the development of breast cancer [44, 3, 19, 38]. For breast density assessment or lesion localization, computer-assisted MR image evaluation requires accurate separation of breast volume from other tissues and regions of the body. The breast–air boundary is identified by searching for a sharp increase in the image intensity from the air side provided that the background noise is low [9, 13, 22]. Detection of the breast–chest wall boundary is a complicated problem, due to coil-related intensity inhomogeneity artefacts and partial volume issues, especially in the presence of dense breast tissue connected to the chest wall muscles and liver tissue beneath chest wall muscles [47]
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