中文标题#
DyMorph-B2I：用於腎臟病理學的動態和形態引導的二值化到實例分割
英文标题#
DyMorph-B2I: Dynamic and Morphology-Guided Binary-to-Instance Segmentation for Renal Pathology
中文摘要#
準確的腎臟病理功能單元形態量化依賴於實例級分割，然而現有的大多數數據集和自動方法僅提供二值（語義）掩碼，限制了下游分析的精度。儘管經典的後處理技術如分水嶺、形態學操作和骨架化常用於將語義掩碼分離為實例，但它們在腎臟組織中發現的多樣化形態和複雜連接性面前，單獨的有效性受到限制。在本研究中，我們提出了 DyMorph-B2I，這是一種針對腎臟病理學的動態、形態引導的二值到實例分割流程。我們的方法在一個統一框架內整合了分水嶺、骨架化和形態學操作，並通過自適應幾何精煉和每個功能單元類別的可定制超參數調整進行補充。通過系統的參數優化，DyMorph-B2I 能夠穩健地分離二值掩碼中存在的粘附和異質結構。實驗結果表明，我們的方法優於單獨的經典方法和簡單的組合，實現了更優的實例分離，並促進了腎臟病理學工作流中更精確的形態計量分析。該流程已在以下位置公開：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：用於腎臟病理學的動態和形態引導的二值化到實例分割
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