Luan Jiang1,2, Xiaoxin Hu3, Qin Xiao3, Yajia Gu3, Qiang Li1,2. 1. Center for Advanced Medical Imaging Technology, Division of Life Sciences, Shanghai Advanced Research Institute, Chinese Academy of Sciences, No. 99 Haike Road, Shanghai, 201210, China. 2. Cooperate Research Center, Shanghai United Imaging Healthcare Co., Ltd., No. 2258 Chengbei Road, Shanghai, 201807, China. 3. Department of Radiology, Shanghai Cancer Hospital of FuDan University, No. 270 DongAn Road, Shanghai, 200032, China.
Abstract
PURPOSE: Amount of fibroglandular tissue (FGT) and level of background parenchymal enhancement (BPE) in breast dynamic contrast enhanced magnetic resonance images (DCE-MRI) are suggested as strong indices for assessing breast cancer risk. Whole breast segmentation is the first important task for quantitative analysis of FGT and BPE in three-dimensional (3-D) DCE-MRI. The purpose of this study is to develop and evaluate a fully automated technique for accurate segmentation of the whole breast in 3-D fat-suppressed DCE-MRI. METHODS: The whole breast segmentation consisted of two major steps, i.e., the delineation of chest wall line and breast skin line. First, a sectional dynamic programming method was employed to trace the upper and/or lower boundaries of the chest wall by use of the positive and/or negative gradient within a band along the chest wall in each 2-D slice. Second, another dynamic programming was applied to delineate the skin-air boundary slice-by-slice based on the saturated gradient of the enhanced image obtained with the prior statistical distribution of gray levels of the breast skin line. Starting from the central slice, these two steps employed a Gaussian function to limit the search range of boundaries in adjacent slices based on the continuity of chest wall line and breast skin line. Finally, local breast skin line detection was applied around armpit to complete the whole breast segmentation. The method was validated with a representative dataset of 100 3-D breast DCE-MRI scans through objective quantification and subjective evaluation. The MR scans in the dataset were acquired with four MR scanners in five spatial resolutions. The cases were assessed with four breast density ratings by radiologists based on Breast Imaging Reporting and Data System (BI-RADS) of American College of Radiology. RESULTS: Our segmentation algorithm achieved a Dice volume overlap measure of 95.8 ± 1.2% and volume difference measure of 8.4 ± 2.4% between the automatically and manually segmented breast regions. Moreover, the root-mean-square distances between the automatically and manually segmented boundaries for the chest wall line and the breast skin line were 0.40 ± 0.15 mm and 0.89 ± 0.21 mm respectively. The segmentation algorithm took approximately 1.0 min to segment the breasts in a MR scan of 160 slices. CONCLUSIONS: 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.
PURPOSE: Amount of fibroglandular tissue (FGT) and level of background parenchymal enhancement (BPE) in breast dynamic contrast enhanced magnetic resonance images (DCE-MRI) are suggested as strong indices for assessing breast cancer risk. Whole breast segmentation is the first important task for quantitative analysis of FGT and BPE in three-dimensional (3-D) DCE-MRI. The purpose of this study is to develop and evaluate a fully automated technique for accurate segmentation of the whole breast in 3-D fat-suppressed DCE-MRI. METHODS: The whole breast segmentation consisted of two major steps, i.e., the delineation of chest wall line and breast skin line. First, a sectional dynamic programming method was employed to trace the upper and/or lower boundaries of the chest wall by use of the positive and/or negative gradient within a band along the chest wall in each 2-D slice. Second, another dynamic programming was applied to delineate the skin-air boundary slice-by-slice based on the saturated gradient of the enhanced image obtained with the prior statistical distribution of gray levels of the breast skin line. Starting from the central slice, these two steps employed a Gaussian function to limit the search range of boundaries in adjacent slices based on the continuity of chest wall line and breast skin line. Finally, local breast skin line detection was applied around armpit to complete the whole breast segmentation. The method was validated with a representative dataset of 100 3-D breast DCE-MRI scans through objective quantification and subjective evaluation. The MR scans in the dataset were acquired with four MR scanners in five spatial resolutions. The cases were assessed with four breast density ratings by radiologists based on Breast Imaging Reporting and Data System (BI-RADS) of American College of Radiology. RESULTS: Our segmentation algorithm achieved a Dice volume overlap measure of 95.8 ± 1.2% and volume difference measure of 8.4 ± 2.4% between the automatically and manually segmented breast regions. Moreover, the root-mean-square distances between the automatically and manually segmented boundaries for the chest wall line and the breast skin line were 0.40 ± 0.15 mm and 0.89 ± 0.21 mm respectively. The segmentation algorithm took approximately 1.0 min to segment the breasts in a MR scan of 160 slices. CONCLUSIONS: 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.
Authors: Leon Lenchik; Laura Heacock; Ashley A Weaver; Robert D Boutin; Tessa S Cook; Jason Itri; Christopher G Filippi; Rao P Gullapalli; James Lee; Marianna Zagurovskaya; Tara Retson; Kendra Godwin; Joey Nicholson; Ponnada A Narayana Journal: Acad Radiol Date: 2019-08-10 Impact factor: 3.173
Authors: Geraldine J Liao; Leah C Henze Bancroft; Roberta M Strigel; Rhea D Chitalia; Despina Kontos; Linda Moy; Savannah C Partridge; Habib Rahbar Journal: J Magn Reson Imaging Date: 2019-04-19 Impact factor: 4.813