Graph-Theoretic Post-Processing of Segmentation With Application to Dense Biofilms.
Graph-Theoretic Post-Processing of Segmentation With Application to Dense Biofilms.
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DOI：
10.1109/tip.2021.3116792
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发表时间：
2021
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最近的深度学习方法为显微镜中的广义细胞分割提供了成功的初始分割结果。然而，对于具有有限的训练基础事实的小细胞的密集排列，深度学习方法会产生过度分割和分割不足的错误。后处理试图平衡细胞计数的全局目标（例如分割）与对所识别细胞的形态的局部保真度之间的权衡。后处理的需要对于在称为生物膜的密集社区中分割3D细菌细胞尤其明显。提出了一种基于图的递归聚类方法m-LCuts，用于自动检测共线结构的聚类，并将其应用于三维细菌生物膜分割中的后处理未解决的细胞。构造去除离群值的图来提取数据中的共线性特征为m-LCut增加了额外的新奇。通过细胞计数评价观察到m-LCuts的优越性，其中超过90%的细胞被正确识别，而平均单细胞分割准确度的下限为0.8。所提出的方法不需要手动指定要分割的细胞的数量。此外，由于数据共线性的存在，m-LCuts对各种应用程序的广泛适应性也使其从其他方法中脱颖而出。
Recent deep learning methods have provided successful initial segmentation results for generalized cell segmentation in microscopy. However, for dense arrangements of small cells with limited ground truth for training, the deep learning methods produce both over-segmentation and under-segmentation errors. Post-processing attempts to balance the trade-off between the global goal of cell counting for instance segmentation, and local fidelity to the morphology of identified cells. The need for post-processing is especially evident for segmenting 3D bacterial cells in densely-packed communities called biofilms. A graph-based recursive clustering approach, m-LCuts, is proposed to automatically detect collinearly structured clusters and applied to post-process unsolved cells in 3D bacterial biofilm segmentation. Construction of outlier-removed graphs to extract the collinearity feature in the data adds additional novelty to m-LCuts. The superiority of m-LCuts is observed by the evaluation in cell counting with over 90% of cells correctly identified, while a lower bound of 0.8 in terms of average single-cell segmentation accuracy is maintained. This proposed method does not need manual specification of the number of cells to be segmented. Furthermore, the broad adaptation for working on various applications, with the presence of data collinearity, also makes m-LCuts stand out from the other approaches.