Evidence map›Paper›PMID 38014263›Full record

ArticlebioRxiv : the preprint server for biology2024

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

Bogdan Kochetov, Phoenix Bell, Paulo S Garcia, Akram S Shalaby, Rebecca Raphael, Benjamin Raymond, Brian J Leibowitz, Karen Schoedel, Rhonda M Brand, Randall E Brand and 6 more

Open access · greenAbstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in bioRxiv : the preprint server for biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed, 1 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

16 authors at 3 institutions in 1 country.

Bogdan KochetovDepartment of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA.
Phoenix BellDepartment of Pathology, University of Pittsburgh, Pittsburgh, PA, USA.
Paulo S GarciaDepartment of Pathology, University of Pittsburgh, Pittsburgh, PA, USA.
Akram S ShalabyUniversity Hospitals Cleveland Medical Center, Case Western Reserve University, Cleveland, OH, USA.
Rebecca RaphaelDepartment of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA.
Benjamin RaymondDepartment of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA.
Brian J LeibowitzUPMC Hillman Cancer Center, Pittsburgh, PA, USA.
Karen SchoedelDepartment of Pathology, University of Pittsburgh, Pittsburgh, PA, USA.
Rhonda M BrandDepartment of Medicine, University of Pittsburgh, Pittsburgh, PA, USA.
Randall E BrandUPMC Hillman Cancer Center, Pittsburgh, PA, USA.
Jian YuDepartment of Medicine, University of Southern California, Los Angeles, CA, USA.
Lin ZhangDepartment of Medicine, University of Southern California, Los Angeles, CA, USA.ORCID 0000-0003-0018-3903
Brenda DiergaardeUPMC Hillman Cancer Center, Pittsburgh, PA, USA.
Robert E SchoenDepartment of Medicine, University of Pittsburgh, Pittsburgh, PA, USA.ORCID 0000-0001-7153-2766
Aatur SinghiDepartment of Pathology, University of Pittsburgh, Pittsburgh, PA, USA.
Shikhar UttamDepartment of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA.ORCID 0000-0002-0897-2918
University of Pittsburgh · USUniversity of Southern California · USUniversity Hospitals of Cleveland · US

Funding

University of Pittsburgh Clinical and Translational Science InstituteUL1TR001857 · NCATS · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI REIS, STEVEN E · 2016 to 2025
$129.3M
NCATS NIH HHS UL1 TR001857
6 · The paper itself

Abstract

Multiplexed imaging technologies have made it possible to interrogate complex tumor microenvironments at sub-cellular resolution within their native spatial context. However, proper quantification of this complexity requires the ability to easily and accurately segment cells into their sub-cellular compartments. Within the supervised learning paradigm, deep learning based segmentation methods demonstrating human level performance have emerged. However, limited work has been done in developing such generalist methods within the label-free unsupervised context. Here we present an unsupervised segmentation (UNSEG) method that achieves deep learning level performance without requiring any training data. UNSEG leverages a Bayesian-like framework and the specificity of nucleus and cell membrane markers to construct an a

Identifiers

PMID38014263
PMCPMC10680584
OpenAlexW4388712787

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.