Africano Gerson, Arponen Otso, Sassi Antti, Rinta-Kiikka Irina, Laaperi Anna-Leena, Pertuz Said
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:1132-1135. doi: 10.1109/EMBC44109.2020.9175960.
CAD systems have shown good potential for improving breast cancer diagnosis and anomaly detection in mammograms. A basic enabling step for the utilization of CAD systems in mammographic analysis is the correct identification of the breast region. Therefore, several methods to segment the pectoral muscle in the medio-lateral oblique (MLO) mammographic view have been proposed in the literature. However, currently it is difficult to perform and objective comparison between different chest wall (CW) detection methods since they are often evaluated with different evaluation procedures, datasets and the implementations of the methods are not publicly available. For this reason, we propose a methodology to evaluate and compare the performance of CW detection methods using a publicly available dataset (INbreast). We also propose a new intensity-based method for automatic CW detection. We then utilize the proposed evaluation methodology to compare the performance of our CW detection algorithm with a state-of-the-art CW detection method. The performance was measured in terms of the Dice's coefficient similarity, the area error and mean contour distance. The proposed method achieves yielded the best results in all the performance measures.
计算机辅助检测（CAD）系统在改善乳腺癌诊断及乳腺X光片中异常检测方面已展现出良好潜力。在乳腺X光分析中利用CAD系统的一个基本关键步骤是正确识别乳腺区域。因此，文献中已提出了多种在内外侧斜位（MLO）乳腺X光视图中分割胸肌的方法。然而，目前不同胸壁（CW）检测方法之间难以进行客观比较，因为它们通常采用不同的评估程序、数据集进行评估，且方法的实现并未公开可用。出于这个原因，我们提出一种方法，使用公开可用数据集（INbreast）来评估和比较CW检测方法的性能。我们还提出了一种基于新强度的自动CW检测方法。然后，我们利用所提出的评估方法，将我们的CW检测算法与一种先进的CW检测方法的性能进行比较。性能通过戴斯系数相似度、面积误差和平均轮廓距离来衡量。所提出的方法在所有性能指标上均取得了最佳结果。
