Baccouche Asma, Garcia-Zapirain Begonya, Castillo Olea Cristian, Elmaghraby Adel S
Department of Computer Science and Engineering, University of Louisville, Louisville, KY, 40292, USA.
eVida Research Group, University of Deusto, Bilbao, 4800, Spain.
NPJ Breast Cancer. 2021 Dec 2;7(1):151. doi: 10.1038/s41523-021-00358-x.
Breast cancer analysis implies that radiologists inspect mammograms to detect suspicious breast lesions and identify mass tumors. Artificial intelligence techniques offer automatic systems for breast mass segmentation to assist radiologists in their diagnosis. With the rapid development of deep learning and its application to medical imaging challenges, UNet and its variations is one of the state-of-the-art models for medical image segmentation that showed promising performance on mammography. In this paper, we propose an architecture, called Connected-UNets, which connects two UNets using additional modified skip connections. We integrate Atrous Spatial Pyramid Pooling (ASPP) in the two standard UNets to emphasize the contextual information within the encoder-decoder network architecture. We also apply the proposed architecture on the Attention UNet (AUNet) and the Residual UNet (ResUNet). We evaluated the proposed architectures on two publically available datasets, the Curated Breast Imaging Subset of Digital Database for Screening Mammography (CBIS-DDSM) and INbreast, and additionally on a private dataset. Experiments were also conducted using additional synthetic data using the cycle-consistent Generative Adversarial Network (CycleGAN) model between two unpaired datasets to augment and enhance the images. Qualitative and quantitative results show that the proposed architecture can achieve better automatic mass segmentation with a high Dice score of 89.52%, 95.28%, and 95.88% and Intersection over Union (IoU) score of 80.02%, 91.03%, and 92.27%, respectively, on CBIS-DDSM, INbreast, and the private dataset.
乳腺癌分析意味着放射科医生要检查乳房X光片以检测可疑的乳房病变并识别肿块肿瘤。人工智能技术提供了用于乳房肿块分割的自动系统，以协助放射科医生进行诊断。随着深度学习的快速发展及其在医学成像挑战中的应用，UNet及其变体是医学图像分割的先进模型之一，在乳房X光检查中表现出了良好的性能。在本文中，我们提出了一种名为Connected-UNets的架构，它使用额外的修改后的跳跃连接来连接两个UNet。我们在两个标准的UNet中集成了空洞空间金字塔池化（ASPP），以强调编码器-解码器网络架构中的上下文信息。我们还将所提出的架构应用于注意力UNet（AUNet）和残差UNet（ResUNet）。我们在两个公开可用的数据集上评估了所提出的架构，即数字乳房X光筛查数据库（CBIS-DDSM）的精选乳房成像子集和INbreast，此外还在一个私有数据集上进行了评估。还使用循环一致生成对抗网络（CycleGAN）模型在两个未配对的数据集之间使用额外的合成数据进行了实验，以增强和提升图像。定性和定量结果表明，所提出的架构在CBIS-DDSM、INbreast和私有数据集上分别可以实现更好的自动肿块分割，Dice分数分别为89.52%、95.28%和95.88%，交并比（IoU）分数分别为80.02%、91.03%和92.27%。
