A common task in the analysis of point patterns is to estimate the intensity of the underlying process, i.e., the expected number of points per unit area or volume at each position over space. In biological studies, a specific feature of point patterns and processes is their confinement within bounded domains of ${\mathbb{R}}^d$ because of the organization of biological systems into various compartments (tissues, cells, nuclei, etc.). This induces a systematic negative bias in intensity estimates at the boundary of the confinement domain. Here, we address this edge effect issue in the context of intensity estimation from multiple realizations, which is particularly relevant to biological studies because of experimental replications. We introduce an edge correction method for a recently proposed maximum likelihood intensity estimator based on distance statistics that requires no additional parameter in the estimation method. We describe corrections relying on locally planar or spherical shape approximations of the domain boundary. Based on corrected estimators, we propose a strategy for the statistical mapping of a 3D bounded process that adapts to the shape of the domain boundary. We demonstrate quantitatively the robustness of the correction method using point patterns simulated over domains of ${\mathbb{R}}^d$ with various shapes. We illustrate the practical usefulness of the method by analyzing the 3D spatial organization of compartments within plant cells. The method should be useful for the statistical analysis of biological structures at different scales.

点图案分析中的一项常见任务是估计基础过程的强度，即在空间上每个位置的单位面积或体积的预期点数。在生物学研究中，点模式和过程的特定特征是将其限制在分子的有界域内${\mathbb{[R}}^d$因为生物系统组织到各个部分（组织，细胞，细胞核等）。这在限制域的边界处在强度估计中引起系统的负偏差。在这里，我们从多个实现的强度估计的背景下解决了这个边缘效应问题，由于实验重复，这与生物学研究特别相关。我们介绍了一种基于距离统计的最近提出的最大似然强度估计器的边缘校正方法，该估计方法中不需要其他参数。我们描述了依赖于域边界的局部平面或球形近似的校正。根据校正后的估算器，我们提出一种3D有界过程的统计映射策略，以适应域边界的形状。我们使用点域上模拟的点模式定量地证明了校正方法的鲁棒性${\mathbb{[R}}^d$具有各种形状。我们通过分析植物细胞内区室的3D空间组织，说明了该方法的实际实用性。该方法应可用于不同规模的生物结构的统计分析。