Data-driven cell tracking and segmentation methods in biomedical imaging require diverse and information-rich training data. In cases where the number of training samples is limited, synthetic computer-generated data sets can be used to improve these methods. This requires the synthesis of cell shapes as well as corresponding microscopy images using generative models. To synthesize realistic living cell shapes, the shape representation used by the generative model should be able to accurately represent fine details and changes in topology, which are common in cells. These requirements are not met by 3D voxel masks, which are restricted in resolution, and polygon meshes, which do not easily model processes like cell growth and mitosis. In this work, we propose to represent living cell shapes as level sets of signed distance functions (SDFs) which are estimated by neural networks. We optimize a fully-connected neural network to provide an implicit representation of the SDF value at any point in a 3D+time domain, conditioned on a learned latent code that is disentangled from the rotation of the cell shape. We demonstrate the effectiveness of this approach on cells that exhibit rapid deformations (Platynereis dumerilii), cells that grow and divide (C. elegans), and cells that have growing and branching filopodial protrusions (A549 human lung carcinoma cells). A quantitative evaluation using shape features, Hausdorff distance, and Dice similarity coefficients of real and synthetic cell shapes shows that our model can generate topologically plausible complex cell shapes in 3D+time with high similarity to real living cell shapes. Finally, we show how microscopy images of living cells that correspond to our generated cell shapes can be synthesized using an image-to-image model.

翻译：生物医学成像中基于数据的细胞追踪与分割方法需要多样且信息丰富的训练数据。当训练样本数量有限时，合成计算机生成的数据集可用于改进这些方法。这要求利用生成模型合成细胞形状及对应的显微图像。为生成逼真的活细胞形状，生成模型采用的形状表示需能精确表征细胞中常见的精细细节与拓扑变化。三维体素掩膜受限于分辨率，而多边形网格难以建模细胞生长与有丝分裂等过程，均无法满足上述要求。本文提出将活细胞形状表示为由神经网络估计的有符号距离函数（SDF）水平集。我们优化全连接神经网络，在三维+时间域中任意点处提供SDF值的隐式表示，并基于与细胞形状旋转解耦的潜在编码进行条件约束。在快速变形细胞（海蠕虫）、生长分裂细胞（秀丽隐杆线虫）以及具有生长分支丝状伪足的细胞（A549人肺癌细胞）上，我们验证了该方法的效果。通过形状特征、豪斯多夫距离及真实与合成细胞形状的Dice相似系数量化评估，表明模型可生成三维+时间域中拓扑合理的复杂细胞形状，与真实活细胞形状高度相似。最后，我们展示了利用图像到图像模型合成对应生成细胞形状的活细胞显微图像的方法。