Abstract
Three-dimensional (3D) structural changes and spatial relationships of micro-anatomic objects in whole-slide digital pathology images encode a large wealth of information on normal tissue development and disease progression. In this paper, we present a complete framework for quantitative spatial analytics of 3D micro-anatomic objects with pathology image volumes, with special focus on vessels and nuclei. Reconstructing 3D vessel structures from a sequence of whole-slide images, we simulate 3D biological systems by generating 3D nuclei uniformly distributed around 3D vessels. Given nuclei are distributed around vessels, intersection detection with 3D nuclei and vessels is conducted by a heuristic algorithm with data structure Axis-Aligned Bounding-Box (AABB). Motivated by real-world use case, we also travel the AABB tree constructed from 3D vessel structures to perform the distance-based query between 3D nuclei and vessels. We quantitatively evaluate the performance of 3D intersection detection by the heuristic algorithm and distance-based query based on AABB tree traversal. Experimental results demonstrate the efficiency of our framework for 3D spatial analytics with whole-slide serial pathology image dataset.
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This research is supported in part by grants from National Science Foundation ACI 1443054 and IIS 1350885, and National Institute of Health K25CA181503.
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Liang, Y., Kong, J., Zhu, Y., Wang, F. (2016). Three-Dimensional Data Analytics for Pathology Imaging. In: Wang, F., Luo, G., Weng, C., Khan, A., Mitra, P., Yu, C. (eds) Biomedical Data Management and Graph Online Querying. Big-O(Q) DMAH 2015 2015. Lecture Notes in Computer Science(), vol 9579. Springer, Cham. https://doi.org/10.1007/978-3-319-41576-5_8
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