DyMorph-B2I: Dynamic and Morphology-Guided Binary-to-Instance Segmentation for Renal Pathology

2025-08-20

AI Summary

Existing kidney pathology datasets typically provide only binary semantic segmentation masks, which are insufficient for precise morphometric analysis; conventional post-processing methods—such as watershed segmentation and skeletonization—lack robustness in separating adherent functional renal units. To address this, we propose a dynamic, morphology-guided framework for converting binary masks into instance-level segmentations. Our approach unifies watershed, skeletonization, and morphological operations within a single pipeline, augmented by an adaptive geometric refinement mechanism and class-specific hyperparameter optimization to accommodate the structural heterogeneity of glomeruli, tubules, and other renal components. Experimental results demonstrate that our method significantly outperforms both individual and naively combined traditional techniques, achieving more accurate instance separation across diverse renal functional units. Consequently, downstream morphometric analyses exhibit markedly improved quantitative accuracy.

Technology Category

• Computer Vision: Segmentation
• Search and Optimization: Mixed Discrete/Continuous Search
• Machine Learning: Multi-instance/Multi-view Learning

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Web Mining and Content Analysis: Bridging structured and unstructured data

Abstract

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 naïve 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.

Problem

• Converts binary segmentation masks to instance-level segmentation
• Addresses diverse morphologies in renal pathology tissue
• Separates adherent structures for accurate morphometric analysis

Innovation

• Unified framework integrating watershed, skeletonization, morphological operations
• Adaptive geometric refinement for each functional unit class
• Systematic parameter optimization for robust instance separation