Author

Rhonda M. Brand

University of PittsburghORCID 0000-0002-3963-6909Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

Click to filter author papers

Signature Assays, Organisms & Methods

Click to filter · or open tag

Assay:imaging· 2 Research Focus:cell type annotation· 1 Approach:experimental assay· 1 Organism:human· 1 Research Focus:spatial domain detection· 1 Assay:spatial transcriptomics· 1 Approach:statistical model· 1

Frequent Co-Authors

Randall E. BrandiD· 2 shared Aatur Dilip SinghiiD· 1 shared Akram S. ShalabyiD· 1 shared Benjamin Raymond· 1 shared Beverley A. MooreiD· 1 shared Bogdan KochetoviD· 1 shared Brenda B. DiergaardeiD· 1 shared Brian J. LeibowitziD· 1 shared

Activity by Year

2024-2025 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 27 mutual co-author links

Click any collaborator node to inspect or filter shared papers

Focal author (center)Selected co-authorORCID co-authorUnlinked name

Curved chords = mutual co-authorship

2 shared papersBioinformatics

Randall E. BrandORCID

Mutual co-authors in cluster

Author profile →

2 papers

Recent·Most cited

A multi-modal whole-slide image processing pipeline for quantitative mapping of tissue architecture, histopathology, and tissue microenvironment

Maomao Chen, Hongqiang Ma, Xuejiao Sun, Marc Schwartz, Randall E. Brand, Jianquan Xu, Dimitrios S. Gotsis, Phuong Nguyen, Beverley A. Moore, Lori Snyder, Rhonda M. Brand, Yang Liu

The paper introduces a multi-modal image processing pipeline for quantitative spatial analysis of tissue architecture, histopathology, and microenvironment using multi-scale imaging techniques.

bioRxiv · Mar 5, 2025 · PreprintBioinformatics

UNSEG: unsupervised segmentation of cells and their nuclei in complex tissue samples

Bogdan Kochetov, Phoenix D. Bell, Paulo S. Garcia, Akram S. Shalaby, Rebecca Raphael, Benjamin Raymond, Brian J. Leibowitz, Karen Elizabeth Schoedel, Rhonda M. Brand, Randall E. Brand, Jian Yu, Lin Zhang, Brenda B. Diergaarde, Robert E. Schoen, Aatur Dilip Singhi, Shikhar Uttam

UNSEG provides an unsupervised method for segmenting cells and their nuclei in complex tissue samples without training data, outperforming deep learning methods in generalization and consistency.

Communications Biology · Aug 30, 2024Peer-reviewedBioinformatics