Milenković Jana, Chambers Olga, Marolt Mušič Maja, Tasič Jurij Franc
Faculty for Electrical Engineering, University of Ljubljana, Tržaška 25, 1000 Ljubljana, Slovenia; Faculty of Medicine, University of Ljubljana, Vrazov trg 2, 1000 Ljubljana, Slovenia.
Jožef Stefan Institute, Jamova 39, 1000 Ljubljana, Slovenia.
Comput Biol Med. 2015 Jul;62:55-64. doi: 10.1016/j.compbiomed.2015.04.001. Epub 2015 Apr 11.
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.
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.
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.
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.
本研究的目的是开发一种强大的乳腺区域分割方法，该方法独立于乳腺区域与周围胸壁和皮肤之间的可见对比度。
提出了一种在轴向磁共振图像中分割乳腺区域的全自动方法，该方法依赖于通过应用可调谐伽柏滤波器获得的边缘图（EM），该滤波器根据局部磁共振图像特征设置其参数，以检测具有相似磁共振信号强度的不同组织之间不可见的过渡。该方法通过结合一种新颖的成本函数应用最短路径搜索技术，该成本函数使用基于相邻切片的边界信息获得的边界搜索区域内的EM信息。在覆盖美国放射学会乳腺影像报告和数据系统（BI-RADS）全乳腺密度范围的52次磁共振扫描上对其进行了验证。
所得结果表明，与针对轴向磁共振图像提出的文献方法相比，该方法对于具有挑战性的情况具有鲁棒性且适用，即部分纤维腺体组织与胸壁和/或皮肤相连且它们之间没有可见对比度，即没有脂肪存在。就骰子相似系数而言，自动获得的和手动获得的乳腺区域分割之间的总体一致性为96.1%，就平均偏差距离而言，乳腺-胸壁和乳腺-皮肤边界划定的一致性分别为1.9毫米和1.2毫米。
所提出方法的准确性、鲁棒性以及对具有挑战性情况的适用性表明其有潜力被纳入计算机辅助分析系统，以支持医生进行决策。
