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

arXiv: https://arxiv.org/abs/2508.15208 · HTML: https://mlsanity.com/paper/2508.15208/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 21 references that are in the map.
- First posted on arXiv 21 Aug 2025.

## Builds on (most cited first, 11 of 11)

- [Segment Anything Model (SAM) for Digital Pathology: Assess Zero-shot Segmentation on Whole Slide Imaging](https://mlsanity.com/paper/2304.04155/) (2023)
- [Fully Convolutional Instance-aware Semantic Segmentation](https://mlsanity.com/paper/1611.07709/) (2016)
- [Automatic detection of rare pathologies in fundus photographs using few-shot learning](https://mlsanity.com/paper/1907.09449/) (2019)
- [Omni-Seg: A Scale-aware Dynamic Network for Renal Pathological Image Segmentation](https://mlsanity.com/paper/2206.13632/) (2022)
- [PrPSeg: Universal Proposition Learning for Panoramic Renal Pathology Segmentation](https://mlsanity.com/paper/2402.19286/) (2024)
- [Incorporating intratumoral heterogeneity into weakly-supervised deep learning models via variance pooling](https://mlsanity.com/paper/2206.08885/) (2022)
- [Democratizing Pathological Image Segmentation with Lay Annotators via Molecular-empowered Learning](https://mlsanity.com/paper/2306.00047/) (2023)
- [Glo-In-One-v2: Holistic Identification of Glomerular Cells, Tissues, and Lesions in Human and Mouse Histopathology](https://mlsanity.com/paper/2411.16961/) (2024)
- [PySpatial: A High-Speed Whole Slide Image Pathomics Toolkit](https://mlsanity.com/paper/2501.06151/) (2025)
- [Spatial Pathomics Toolkit for Quantitative Analysis of Podocyte Nuclei with Histology and Spatial Transcriptomics Data in Renal Pathology](https://mlsanity.com/paper/2308.06288/) (2023)
- [Cross-Species Data Integration for Enhanced Layer Segmentation in Kidney Pathology](https://mlsanity.com/paper/2408.09278/) (2024)

## Built on by (most cited first, 1 of 1)

- [MORI-Seg: Learning Morphological Geometry for Instance Segmentation without Instance Annotations](https://mlsanity.com/paper/2605.28261/) (2026)

## Abstract (arXiv)

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.