中文标题#
DyMorph-B2I：用于肾脏病理学的动态和形态引导的二值化到实例分割
英文标题#
DyMorph-B2I: Dynamic and Morphology-Guided Binary-to-Instance Segmentation for Renal Pathology
中文摘要#
准确的肾脏病理功能单元形态量化依赖于实例级分割，然而现有的大多数数据集和自动方法仅提供二值（语义）掩码，限制了下游分析的精度。 尽管经典的后处理技术如分水岭、形态学操作和骨架化常用于将语义掩码分离为实例，但它们在肾脏组织中发现的多样化形态和复杂连接性面前，单独的有效性受到限制。 在本研究中，我们提出了 DyMorph-B2I，这是一种针对肾脏病理学的动态、形态引导的二值到实例分割流程。 我们的方法在一个统一框架内整合了分水岭、骨架化和形态学操作，并通过自适应几何精炼和每个功能单元类别的可定制超参数调整进行补充。 通过系统的参数优化，DyMorph-B2I 能够稳健地分离二值掩码中存在的粘附和异质结构。 实验结果表明，我们的方法优于单独的经典方法和简单的组合，实现了更优的实例分离，并促进了肾脏病理学工作流中更精确的形态计量分析。 该流程已在以下位置公开：https://github.com/ddrrnn123/DyMorph-B2I.
英文摘要#
Accurate morphological quantification of renal pathology functional units relies on instance-level segmentation, yet most existing datasets and automated methods provide only binary (semantic) masks, limiting the precision of downstream analyses. Although classical post-processing techniques such as watershed, morphological operations, and skeletonization, are often used to separate semantic masks into instances, their individual effectiveness is constrained by the diverse morphologies and complex connectivity found in renal tissue. In this study, we present DyMorph-B2I, a dynamic, morphology-guided binary-to-instance segmentation pipeline tailored for renal pathology. Our approach integrates watershed, skeletonization, and morphological operations within a unified framework, complemented by adaptive geometric refinement and customizable hyperparameter tuning for each class of functional unit. Through systematic parameter optimization, DyMorph-B2I robustly separates adherent and heterogeneous structures present in binary masks. Experimental results demonstrate that our method outperforms individual classical approaches and na"ive combinations, enabling superior instance separation and facilitating more accurate morphometric analysis in renal pathology workflows. The pipeline is publicly available at: https://github.com/ddrrnn123/DyMorph-B2I.
文章页面#
DyMorph-B2I：用于肾脏病理学的动态和形态引导的二值化到实例分割
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