Connected-UNets: a deep learning architecture for breast mass segmentation.
Connected-UNets: a deep learning architecture for breast mass segmentation.
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Connected-UNets：用于乳腺肿块分割的深度学习架构。
DOI：
10.1038/s41523-021-00358-x
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发表时间：
2021-12-02
影响因子：
5.9
通讯作者：
Elmaghraby AS
中科院分区：
文献类型：
--
作者：
Baccouche A;Garcia-Zapirain B;Castillo Olea C;Elmaghraby AS
乳腺癌分析意味着放射科医生检查乳房x光片以发现可疑的乳房病变并确定肿块。人工智能技术为乳房质量分割提供了自动系统，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中的应用所面临的挑战，UNet及其变体是最先进的医学图像分割模型之一，在乳房x光检查中显示出很好的性能。在本文中，我们提出了一种被称为connected - unet的架构，它使用附加的修改跳过连接连接两个unet。我们将空间金字塔池（ASPP）集成到两个标准UNets中，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单元（AUNet）和残差单元（ResUNet）。我们在两个公开可用的数据集上评估了拟议的架构，一个是乳腺筛查数字数据库（CBIS-DDSM）和INbreast，另外一个是私人数据集。实验还使用额外的合成数据，在两个未配对的数据集之间使用循环一致生成对抗网络（CycleGAN）模型来增强和增强图像。定性和定量结果表明，该架构在CBIS-DDSM、INbreast和私有数据集上均能实现较好的自动海量分割，Dice得分分别为89.52%、95.28%和95.88%，IoU得分分别为80.02%、91.03%和92.27%。
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.
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影响因子：
3
作者：
Abdelhafiz D;Bi J;Ammar R;Yang C;Nabavi S
通讯作者：
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影响因子：
9.8
作者：
Lee RS;Gimenez F;Hoogi A;Miyake KK;Gorovoy M;Rubin DL
通讯作者：
Rubin DL
影响因子：
3.3
作者：
Becker, Anton S.;Jendele, Lukas;Konukoglu, Ender
通讯作者：
Konukoglu, Ender
影响因子：
10.9
作者：
Dhungel, Neeraj;Carneiro, Gustavo;Bradley, Andrew P.
通讯作者：
Bradley, Andrew P.
DOI：
10.1016/j.ijmedinf.2018.06.003
发表时间：
2018-09-01
影响因子：
4.9
作者：
Al-antari, Mugahed A.;Al-masni, Mohammed A.;Kim, Tae-Seong
通讯作者：
Kim, Tae-Seong