SPECHT: Self-tuning Plausibility based object detection Enables quantification of Conflict in Heterogeneous multi-scale microscopy.
SPECHT: Self-tuning Plausibility based object detection Enables quantification of Conflict in Heterogeneous multi-scale microscopy.
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DOI：
10.1371/journal.pone.0276726
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发表时间：
2022
期刊：
影响因子：
3.7
通讯作者：
Hamarneh, Ghassan
中科院分区：
文献类型：
--
作者：
Cardoen, Ben;Wong, Timothy;Alan, Parsa;Lee, Sieun;Matsubara, Joanne Aiko;Nabi, Ivan Robert;Hamarneh, Ghassan
在荧光显微镜中识别小物体是一个不平凡的任务，由参数敏感的算法，其中有一个明确的需要，动态适应不断变化的成像条件的方法。在这里，我们介绍了一种自适应对象检测方法，给定的显微图像和图像级别标签，使用基于峰度的匹配的图像差分的分布来表达操作员的意图在召回率或精度。我们展示了如何在特征空间中的统计距离的理论上界，使应用的信念理论，以获得统计支持每个检测到的对象，捕捉这些方面的图像，支持标签，并在何种程度上。我们在2个数据集上验证了我们的方法：通过受激发射损耗（STED）超分辨率显微镜区分亚衍射极限小窝和支架;以及在神经病理学证实的阿尔茨海默病供体组织的共聚焦显微镜视网膜横截面中检测淀粉样蛋白-β沉积。我们的研究结果与生物学基础事实和以前的亚细胞对象分类结果一致，并增加了对更细微的类别转换动态的洞察。我们说明了新的应用程序的信念理论的对象检测在异构显微镜数据集和量化的证据冲突的联合信念函数。通过将我们的方法成功地应用于组织切片的衍射限制共焦成像和亚细胞结构的超分辨率显微镜，我们证明了多尺度的适用性。
Identification of small objects in fluorescence microscopy is a non-trivial task burdened by parameter-sensitive algorithms, for which there is a clear need for an approach that adapts dynamically to changing imaging conditions. Here, we introduce an adaptive object detection method that, given a microscopy image and an image level label, uses kurtosis-based matching of the distribution of the image differential to express operator intent in terms of recall or precision. We show how a theoretical upper bound of the statistical distance in feature space enables application of belief theory to obtain statistical support for each detected object, capturing those aspects of the image that support the label, and to what extent. We validate our method on 2 datasets: distinguishing sub-diffraction limit caveolae and scaffold by stimulated emission depletion (STED) super-resolution microscopy; and detecting amyloid-β deposits in confocal microscopy retinal cross-sections of neuropathologically confirmed Alzheimer’s disease donor tissue. Our results are consistent with biological ground truth and with previous subcellular object classification results, and add insight into more nuanced class transition dynamics. We illustrate the novel application of belief theory to object detection in heterogeneous microscopy datasets and the quantification of conflict of evidence in a joint belief function. By applying our method successfully to diffraction-limited confocal imaging of tissue sections and super-resolution microscopy of subcellular structures, we demonstrate multi-scale applicability.
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