IEEE Trans Biomed Eng. 2018 Mar;65(3):625-634. doi: 10.1109/TBME.2017.2711529.
Detection of nuclei is an important step in phenotypic profiling of 1) histology sections imaged in bright field; and 2) colony formation of the 3-D cell culture models that are imaged using confocal microscopy. It is shown that feature-based representation of the original image improves color decomposition (CD) and subsequent nuclear detection using convolutional neural networks independent of the imaging modality. The feature-based representation utilizes the Laplacian of Gaussian (LoG) filter, which accentuates blob-shape objects. Moreover, in the case of samples imaged in bright field, the LoG response also provides the necessary initial statistics for CD using nonnegative matrix factorization. Several permutations of input data representations and network architectures are evaluated to show that by coupling improved CD and the LoG response of this representation, detection of nuclei is advanced. In particular, the frequencies of detection of nuclei with the vesicular or necrotic phenotypes, or poor staining, are improved. The overall system has been evaluated against manually annotated images, and the F-scores for alternative representations and architectures are reported.
细胞核检测是对明场成像的组织学切片进行表型分析和对使用共聚焦显微镜成像的 3D 细胞培养模型的集落形成进行分析的重要步骤。研究表明，基于特征的原始图像表示可以改善颜色分解 (CD) 和随后使用卷积神经网络进行的核检测，而与成像方式无关。基于特征的表示利用拉普拉斯高斯 (LoG) 滤波器，突出了斑点形状的物体。此外，在明场成像的样本中，LoG 响应还为使用非负矩阵分解的 CD 提供了必要的初始统计信息。评估了几种输入数据表示和网络架构的排列，以表明通过结合改进的 CD 和该表示的 LoG 响应，可以提高核检测的性能。特别是，提高了对具有囊泡或坏死表型或染色不良的核的检测频率。该系统已针对手动标注图像进行了评估，并报告了替代表示和架构的 F 分数。
