论文标题
使用双向对抗网络的几何匹配的多源显微图像合成
Geometrically Matched Multi-source Microscopic Image Synthesis Using Bidirectional Adversarial Networks
论文作者
论文摘要
来自多种模式的微观图像可以产生大量的实验信息。实际上，在给定的观察期下的生物学或物理约束可能会阻止研究人员获得足够的显微镜扫描。最近的研究表明，图像合成是释放这种约束的流行方法之一。但是，大多数现有的合成方法仅将图像从源域转化为目标域而没有固体几何关联。为了应对这一挑战，我们提出了一种创新的模型架构Banis，以从具有不同几何特征的多源域中综合多元化的显微镜图像。实验结果表明，Banis成功合成了秀丽隐杆线虫显微镜胚胎图像上有利的图像对。据我们所知，Banis是合成显微镜图像的第一个应用，该图像将不同的空间几何特征与多源域相关联。
Microscopic images from multiple modalities can produce plentiful experimental information. In practice, biological or physical constraints under a given observation period may prevent researchers from acquiring enough microscopic scanning. Recent studies demonstrate that image synthesis is one of the popular approaches to release such constraints. Nonetheless, most existing synthesis approaches only translate images from the source domain to the target domain without solid geometric associations. To embrace this challenge, we propose an innovative model architecture, BANIS, to synthesize diversified microscopic images from multi-source domains with distinct geometric features. The experimental outcomes indicate that BANIS successfully synthesizes favorable image pairs on C. elegans microscopy embryonic images. To the best of our knowledge, BANIS is the first application to synthesize microscopic images that associate distinct spatial geometric features from multi-source domains.
