Jana Milenković1, Olga Chambers2, Maja Marolt Mušič3, Jurij Franc Tasič4. 1. Faculty for Electrical Engineering, University of Ljubljana, Tržaška 25, 1000 Ljubljana, Slovenia; Faculty of Medicine, University of Ljubljana, Vrazov trg 2, 1000 Ljubljana, Slovenia. Electronic address: jana.milenkovic@fe.uni-lj.si. 2. Jožef Stefan Institute, Jamova 39, 1000 Ljubljana, Slovenia. 3. Institute of Oncology, Zaloška 2, 1000 Ljubljana, Slovenia. 4. Faculty for Electrical Engineering, University of Ljubljana, Tržaška 25, 1000 Ljubljana, Slovenia.
Abstract
PURPOSE: The purpose of this study was to develop a robust breast-region segmentation method independent from the visible contrast between the breast region and surrounding chest wall and skin. MATERIALS AND METHODS: A fully-automated method for segmentation of the breast region in the axial MR images is presented relying on the edge map (EM) obtained by applying a tunable Gabor filter which sets its parameters according to the local MR image characteristics to detect non-visible transitions between different tissues having a similar MRI signal intensity. The method applies the shortest-path search technique by incorporating a novel cost function using the EM information within the border-search area obtained based on the border information from the adjacent slice. It is validated on 52 MRI scans covering the full American College of Radiology Breast Imaging-Reporting and Data System (BI-RADS) breast-density range. RESULTS: The obtained results indicate that the method is robust and applicable for the challenging cases where a part of the fibroglandular tissue is connected to the chest wall and/or skin with no visible contrast, i.e. no fat presence, between them compared to the literature methods proposed for the axial MR images. The overall agreement between automatically- and manually-obtained breast-region segmentations is 96.1% in terms of the Dice Similarity Coefficient, and for the breast-chest wall and breast-skin border delineations it is 1.9mm and 1.2mm, respectively, in terms of the Mean-Deviation Distance. CONCLUSION: The accuracy, robustness and applicability for the challenging cases of the proposed method show its potential to be incorporated into computer-aided analysis systems to support physicians in their decision making.
PURPOSE: The purpose of this study was to develop a robust breast-region segmentation method independent from the visible contrast between the breast region and surrounding chest wall and skin. MATERIALS AND METHODS: A fully-automated method for segmentation of the breast region in the axial MR images is presented relying on the edge map (EM) obtained by applying a tunable Gabor filter which sets its parameters according to the local MR image characteristics to detect non-visible transitions between different tissues having a similar MRI signal intensity. The method applies the shortest-path search technique by incorporating a novel cost function using the EM information within the border-search area obtained based on the border information from the adjacent slice. It is validated on 52 MRI scans covering the full American College of Radiology Breast Imaging-Reporting and Data System (BI-RADS) breast-density range. RESULTS: The obtained results indicate that the method is robust and applicable for the challenging cases where a part of the fibroglandular tissue is connected to the chest wall and/or skin with no visible contrast, i.e. no fat presence, between them compared to the literature methods proposed for the axial MR images. The overall agreement between automatically- and manually-obtained breast-region segmentations is 96.1% in terms of the Dice Similarity Coefficient, and for the breast-chest wall and breast-skin border delineations it is 1.9mm and 1.2mm, respectively, in terms of the Mean-Deviation Distance. CONCLUSION: The accuracy, robustness and applicability for the challenging cases of the proposed method show its potential to be incorporated into computer-aided analysis systems to support physicians in their decision making.
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