Aliniya Parvaneh, Nicolescu Mircea, Nicolescu Monica, Bebis George
Computer Science and Engineering Department, College of Engineering, University of Nevada, Reno, Main Campus, Reno, NV 89557, USA.
J Imaging. 2024 Dec 22;10(12):331. doi: 10.3390/jimaging10120331.
Mammography images are the most commonly used tool for breast cancer screening. The presence of pectoral muscle in images for the mediolateral oblique view makes designing a robust automated breast cancer detection system more challenging. Most of the current methods for removing the pectoral muscle are based on traditional machine learning approaches. This is partly due to the lack of segmentation masks of pectoral muscle in available datasets. In this paper, we provide the segmentation masks of the pectoral muscle for the INbreast, MIAS, and CBIS-DDSM datasets, which will enable the development of supervised methods and the utilization of deep learning. Training deep learning-based models using segmentation masks will also be a powerful tool for removing pectoral muscle for unseen data. To test the validity of this idea, we trained AU-Net separately on the INbreast and CBIS-DDSM for the segmentation of the pectoral muscle. We used cross-dataset testing to evaluate the performance of the models on an unseen dataset. In addition, the models were tested on all of the images in the MIAS dataset. The experimental results show that cross-dataset testing achieves a comparable performance to the same-dataset experiments.
乳腺钼靶图像是乳腺癌筛查最常用的工具。在内外侧斜位图像中存在胸肌，这使得设计一个强大的自动化乳腺癌检测系统更具挑战性。当前大多数去除胸肌的方法基于传统机器学习方法。部分原因是现有数据集中缺乏胸肌的分割掩码。在本文中，我们为INbreast、MIAS和CBIS-DDSM数据集提供了胸肌的分割掩码，这将有助于开发监督方法并利用深度学习。使用分割掩码训练基于深度学习的模型也将成为去除未知数据中胸肌的强大工具。为了测试这一想法的有效性，我们分别在INbreast和CBIS-DDSM上训练了AU-Net用于胸肌分割。我们使用跨数据集测试来评估模型在未知数据集上的性能。此外，还在MIAS数据集的所有图像上对模型进行了测试。实验结果表明，跨数据集测试与同数据集实验取得了相当的性能。
