PDF(Pubmed)
Abstract：
BACKGROUND: Spatial mapping of transcriptional states provides valuable biological insights into cellular functions and interactions in the context of the tissue. Accurate 3D cell segmentation is a critical step in the analysis of this data towards understanding diseases and normal development in situ. Current approaches designed to automate 3D segmentation include stitching masks along one dimension, training a 3D neural network architecture from scratch, and reconstructing a 3D volume from 2D segmentations on all dimensions. However, the applicability of existing methods is hampered by inaccurate segmentations along the non-stitching dimensions, the lack of high-quality diverse 3D training data, and inhomogeneity of image resolution along orthogonal directions due to acquisition constraints; as a result, they have not been widely used in practice.
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.
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.
摘要：
背景：转录状态的空间作图提供了对组织环境中细胞功能和相互作用的有价值的生物学见解。准确的3D细胞分割是分析这些数据以了解疾病和原位正常发育的关键步骤。当前设计用于自动化3D分割的方法包括沿一维拼接掩模，从头开始训练3D神经网络架构，并从所有维度上的2D分割重建3D体积。然而,沿非拼接维度的不准确分割阻碍了现有方法的适用性，缺乏高质量多样的3D训练数据，以及由于采集约束导致的图像分辨率沿正交方向的不均匀性；因此，它们在实践中没有被广泛使用。
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。
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。
