3D GAN image synthesis and dataset quality assessment for bacterial biofilm.
3D GAN image synthesis and dataset quality assessment for bacterial biofilm.
复制标题
细菌生物膜的 3D GAN 图像合成和数据集质量评估。
DOI：
10.1093/bioinformatics/btac529
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
Acton,ScottT
中科院分区：
文献类型：
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作者：
Wang,Jie;Tabassum,Nazia;Toma,TanjinT;Wang,Yibo;Gahlmann,Andreas;Acton,ScottT
数据驱动的深度学习技术通常需要大量的标记训练数据来实现生物图像分析中的可靠解决方案。然而，细菌生物膜图像中的噪声图像条件和高细胞密度使得难以获得3D细胞注释。或者，试图通过合成数据生成的数据增强，但目前的方法无法产生逼真的images.ResultsThis本文提出了一种生物图像合成和评估工作流程与应用程序，以增加细菌生物膜图像。利用具有不平衡循环一致性损失函数的3D循环生成对抗网络（GAN），以便从二进制细胞标签合成3D生物膜图像。然后，提出了一种随机合成数据集质量评估（SSQA）措施，比较统计外观相似性随机补丁从两个数据集的随机图像。SSQA分数和其他现有的图像质量测量都指示所提出的3D循环GAN连同不平衡损失函数一起沿着提供了可靠的现实（如通过平均意见分数所测量的）3D合成生物膜图像。在3D细胞分割实验中，GAN增强的训练模型还提供了更真实的信号与背景强度比，并提高了细胞计数的准确性。可用性和实施https：github.com/jwang-c/DeepBiofilm.Supplementary信息补充数据可在Bioinformatics online获得。
MotivationData-driven deep learning techniques usually require a large quantity of labeled training data to achieve reliable solutions in bioimage analysis. However, noisy image conditions and high cell density in bacterial biofilm images make 3D cell annotations difficult to obtain. Alternatively, data augmentation via synthetic data generation is attempted, but current methods fail to produce realistic images.ResultsThis article presents a bioimage synthesis and assessment workflow with application to augment bacterial biofilm images. 3D cyclic generative adversarial networks (GAN) with unbalanced cycle consistency loss functions are exploited in order to synthesize 3D biofilm images from binary cell labels. Then, a stochastic synthetic dataset quality assessment (SSQA) measure that compares statistical appearance similarity between random patches from random images in two datasets is proposed. Both SSQA scores and other existing image quality measures indicate that the proposed 3D Cyclic GAN, along with the unbalanced loss function, provides a reliably realistic (as measured by mean opinion score) 3D synthetic biofilm image. In 3D cell segmentation experiments, a GAN-augmented training model also presents more realistic signal-to-background intensity ratio and improved cell counting accuracy.Availability and implementationhttps://github.com/jwang-c/DeepBiofilm.Supplementary informationSupplementary data are available atBioinformaticsonline.