论文标题
从密度图中改进了计数和定位，以在2D和3D显微镜成像中进行对象检测
Improved Counting and Localization from Density Maps for Object Detection in 2D and 3D Microscopy Imaging
论文作者
论文摘要
对象计数和定位是在大规模显微镜应用中进行定量分析的关键步骤。当目标对象重叠，密集聚集和/或当前模糊边界时，此过程变得具有挑战性。以前基于深度学习产生密度图的方法通过假设对象计数等于密度图的集成，从而达到了对象计数的高度准确性。但是，当对象在准确的定位上显示出显着重叠时，该模型会失败。我们提出了一种从密度图中计数和本地化对象的替代方法，以克服此限制。我们的过程包括以下三个关键方面：1）基于密度图的统计属性提出一种新的计数方法，2）根据所提出的计数方法优化那些对这些对象进行良好检测的对象的计数结果，3）3）使用所提出的计数方法提高未检测到的对象的定位，以先验信息为先验信息。验证包括以已知地面真理的显微镜数据处理以及与使用密度图的常规处理的其他模型进行比较。我们的结果表明，在2D和3D显微镜数据中对象的计数和定位方面的性能有所提高。此外，考虑到依赖密度图方法的各种应用，提出的方法是通用的。我们的代码将在审视后发布。
Object counting and localization are key steps for quantitative analysis in large-scale microscopy applications. This procedure becomes challenging when target objects are overlapping, are densely clustered, and/or present fuzzy boundaries. Previous methods producing density maps based on deep learning have reached a high level of accuracy for object counting by assuming that object counting is equivalent to the integration of the density map. However, this model fails when objects show significant overlap regarding accurate localization. We propose an alternative method to count and localize objects from the density map to overcome this limitation. Our procedure includes the following three key aspects: 1) Proposing a new counting method based on the statistical properties of the density map, 2) optimizing the counting results for those objects which are well-detected based on the proposed counting method, and 3) improving localization of poorly detected objects using the proposed counting method as prior information. Validation includes processing of microscopy data with known ground truth and comparison with other models that use conventional processing of the density map. Our results show improved performance in counting and localization of objects in 2D and 3D microscopy data. Furthermore, the proposed method is generic, considering various applications that rely on the density map approach. Our code will be released post-review.
