METHODS: To address these challenges, we formulate the problem of finding cell correspondence across layers with a novel optimal transport (OT) approach. We propose CellStitch, a flexible pipeline that segments cells from 3D images without requiring large amounts of 3D training data. We further extend our method to interpolate internal slices from highly anisotropic cell images to recover isotropic cell morphology.
RESULTS: We evaluated the performance of CellStitch through eight 3D plant microscopic datasets with diverse anisotropic levels and cell shapes. CellStitch substantially outperforms the state-of-the art methods on anisotropic images, and achieves comparable segmentation quality against competing methods in isotropic setting. We benchmarked and reported 3D segmentation results of all the methods with instance-level precision, recall and average precision (AP) metrics.
CONCLUSIONS: The proposed OT-based 3D segmentation pipeline outperformed the existing state-of-the-art methods on different datasets with nonzero anisotropy, providing high fidelity recovery of 3D cell morphology from microscopic images.
方法:为了应对这些挑战,我们制定了一个新的最优运输(OT)方法,发现跨层细胞对应的问题。我们提议CellStitch,一个灵活的管道,从3D图像中分割细胞,而不需要大量的3D训练数据。我们进一步扩展了我们的方法,以从高度各向异性的细胞图像中插值内部切片,以恢复各向同性的细胞形态。
结果:我们通过八个具有不同各向异性水平和细胞形状的3D植物微观数据集评估了CellStitch的性能。CellStitch在各向异性图像上的性能大大优于最先进的方法,并实现了与各向同性设置中的竞争方法相当的分割质量。我们以实例级精度对所有方法的3D分割结果进行了基准测试和报告,召回率和平均精度(AP)指标。
结论:提出的基于OT的3D分割管道在具有非零各向异性的不同数据集上优于现有的最新方法,从显微图像提供3D细胞形态的高保真恢复。