Leveraging pre-trained machine learning models for islet quantification in type 1 diabetes

Sanghoon Kang; Jesus D Penaloza Aponte; Omar Elashkar; Juan Francisco Morales; Nicholas Waddington; Damon G Lamb; Huiwen Ju; Martha Campbell-Thompson; Sarah Kim

doi:10.1016/j.jpi.2024.100406

Leveraging pre-trained machine learning models for islet quantification in type 1 diabetes

J Pathol Inform. 2024 Nov 8:16:100406. doi: 10.1016/j.jpi.2024.100406. eCollection 2025 Jan.

Authors

Affiliations

¹ Department of Pharmaceutics, Center for Pharmacometrics and Systems Pharmacology, Intelligent Critical Care Center, College of Pharmacy, University of Florida, Orlando, FL, USA.
² Department of Pathology, Immunology, and Laboratory Medicine, Diabetes Institute, College of Medicine, University of Florida, Gainesville, FL, USA.
³ Department of Neuroscience, McKnight Brain Institute, University of Florida, Gainesville, FL, USA.
⁴ Departments of Psychiatry, Neuroscience, Biomedical Engineering, McKnight Brain Institute, College of Medicine, University of Florida, Gainesville, FL, USA.
⁵ Malcom Randall VAMC, Gainesville, FL, USA.
⁶ NVIDIA Corporation, Santa Clara, CA, USA.

Abstract

Human islets display a high degree of heterogeneity in terms of size, number, architecture, and endocrine cell-type compositions. An ever-increasing number of immunohistochemistry-stained whole slide images (WSIs) are available through the online pathology database of the Network for Pancreatic Organ donors with Diabetes (nPOD) program at the University of Florida (UF). We aimed to develop an enhanced machine learning-assisted WSI analysis workflow to utilize the nPOD resource for analysis of endocrine cell heterogeneity in the natural history of type 1 diabetes (T1D) in comparison to donors without diabetes. To maximize usability, the user-friendly open-source software QuPath was selected for the main interface. The WSI data were analyzed with two pre-trained machine learning models (i.e., Segment Anything Model (SAM) and QuPath's pixel classifier), using the UF high-performance-computing cluster, HiPerGator. SAM was used to define precise endocrine cell and cell grouping boundaries (with an average quality score of 0.91 per slide), and the artificial neural network-based pixel classifier was applied to segment areas of insulin- or glucagon-stained cytoplasmic regions within each endocrine cell. An additional script was developed to automatically count CD3+ cells inside and within 20 μm of each islet perimeter to quantify the number of islets with inflammation (i.e., CD3+ T-cell infiltration). Proof-of-concept analysis was performed to test the developed workflow in 12 subjects using 24 slides. This open-source machine learning-assisted workflow enables rapid and high throughput determinations of endocrine cells, whether as single cells or within groups, across hundreds of slides. It is expected that the use of this workflow will accelerate our understanding of endocrine cell and islet heterogeneity in the context of T1D endotypes and pathogenesis.

Keywords: Digital pathology; Islet heterogeneity; Machine learning; Type 1 diabetes; Whole slide images.