# Glo-In-One-v2: Holistic Identification of Glomerular Cells, Tissues, and Lesions in Human and Mouse Histopathology

arXiv: https://arxiv.org/abs/2411.16961 · HTML: https://mlsanity.com/paper/2411.16961/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 37 references that are in the map.
- First posted on arXiv 25 Nov 2024.

## Builds on (most cited first, 10 of 10)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Segmenter: Transformer for Semantic Segmentation](https://mlsanity.com/paper/2105.05633/) (2021)
- [Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images](https://mlsanity.com/paper/2201.01266/) (2022)
- [DoDNet: Learning to segment multi-organ and tumors from multiple partially labeled datasets](https://mlsanity.com/paper/2011.10217/) (2020)
- [Fast Image Processing with Fully-Convolutional Networks](https://mlsanity.com/paper/1709.00643/) (2017)
- [Iterative annotation to ease neural network training: Specialized machine learning in medical image analysis](https://mlsanity.com/paper/1812.07509/) (2018)
- [CircleNet: Anchor-free Detection with Circle Representation](https://mlsanity.com/paper/2006.02474/) (2020)
- [Compound Figure Separation of Biomedical Images with Side Loss](https://mlsanity.com/paper/2107.08650/) (2021)
- [Glo-In-One: Holistic Glomerular Detection, Segmentation, and Lesion Characterization with Large-scale Web Image Mining](https://mlsanity.com/paper/2206.00123/) (2022)
- [Automatic Fine-grained Glomerular Lesion Recognition in Kidney Pathology](https://mlsanity.com/paper/2203.05847/) (2022)

## Built on by (most cited first, 7 of 7)

- [Glo-VLMs: Leveraging Vision-Language Models for Fine-Grained Diseased Glomerulus Classification](https://mlsanity.com/paper/2508.15960/) (2025)
- [GloPath: An Entity-Centric Foundation Model for Glomerular Lesion Assessment and Clinicopathological Insights](https://mlsanity.com/paper/2603.02926/) (2026)
- [DyMorph-B2I: Dynamic and Morphology-Guided Binary-to-Instance Segmentation for Renal Pathology](https://mlsanity.com/paper/2508.15208/) (2025)
- [M^3-GloDets: Multi-Region and Multi-Scale Analysis of Fine-Grained Diseased Glomerular Detection](https://mlsanity.com/paper/2508.17666/) (2025)
- [From Classification to Cross-Modal Understanding: Leveraging Vision-Language Models for Fine-Grained Renal Pathology](https://mlsanity.com/paper/2511.11984/) (2025)
- [How Close Are We? Limitations and Progress of AI Models in Banff Lesion Scoring](https://mlsanity.com/paper/2510.27158/) (2025)
- [Seeing Abnormal from Normal: Glomerular Abnormality in Representations of Normal Renal Morphology](https://mlsanity.com/paper/2609.19444/) (2026)

## Abstract (arXiv)

Segmenting glomerular intraglomerular tissue and lesions traditionally depends on detailed morphological evaluations by expert nephropathologists, a labor-intensive process susceptible to interobserver variability. Our group previously developed the Glo-In-One toolkit for integrated detection and segmentation of glomeruli. In this study, we leverage the Glo-In-One toolkit to version 2 with fine-grained segmentation capabilities, curating 14 distinct labels for tissue regions, cells, and lesions across a dataset of 23,529 annotated glomeruli across human and mouse histopathology data. To our knowledge, this dataset is among the largest of its kind to date.In this study, we present a single dynamic head deep learning architecture designed to segment 14 classes within partially labeled images of human and mouse pathology data. Our model was trained using a training set derived from 368 annotated kidney whole-slide images (WSIs) to identify 5 key intraglomerular tissues covering Bowman's capsule, glomerular tuft, mesangium, mesangial cells, and podocytes. Additionally, the network segments 9 glomerular lesion classes including adhesion, capsular drop, global sclerosis, hyalinosis, mesangial lysis, microaneurysm, nodular sclerosis, mesangial expansion, and segmental sclerosis. The glomerulus segmentation model achieved a decent performance compared with baselines, and achieved a 76.5 % average Dice Similarity Coefficient (DSC). Additional, transfer learning from rodent to human for glomerular lesion segmentation model has enhanced the average segmentation accuracy across different types of lesions by more than 3 %, as measured by Dice scores. The Glo-In-One-v2 model and trained weight have been made publicly available at https: //github.com/hrlblab/Glo-In-One_v2.