Evidence map›Paper›PMID 37725031›Full record

ArticleCytotherapy2023

Real-time semantic segmentation and anomaly detection of functional images for cell therapy manufacturing.

Rui Qi Chen, Benjamin Joffe, Paloma Casteleiro Costa, Caroline Filan, Bryan Wang, Stephen Balakirsky, Francisco Robles, Krishnendu Roy, Jing Li

Erratum issuedAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Cytotherapy, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Article
  2. Qualitative and quantitative assessment ofBiomedical optics express · 2026
    Article
  3. Article
  4. Article
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors.

Rui Qi ChenH. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA.
Benjamin JoffeGeorgia Tech Research Institute, Georgia Institute of Technology, Atlanta, Georgia, USA.
Paloma Casteleiro CostaWallace H. Coulter Department of Biomedical Engineering at Georgia Tech and Emory University, Atlanta, Georgia, USA.
Caroline FilanWallace H. Coulter Department of Biomedical Engineering at Georgia Tech and Emory University, Atlanta, Georgia, USA.
Bryan WangWallace H. Coulter Department of Biomedical Engineering at Georgia Tech and Emory University, Atlanta, Georgia, USA.
Stephen BalakirskyGeorgia Tech Research Institute, Georgia Institute of Technology, Atlanta, Georgia, USA.
Francisco RoblesWallace H. Coulter Department of Biomedical Engineering at Georgia Tech and Emory University, Atlanta, Georgia, USA.
Krishnendu RoyWallace H. Coulter Department of Biomedical Engineering at Georgia Tech and Emory University, Atlanta, Georgia, USA.
Jing LiH. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA. Electronic address: jli3175@gatech.edu.

Funding

Accessible label-free optical microscopy with quantitative molecular and functional contrastR35GM147437 · NIGMS · ST. JUDE CHILDREN'S RESEARCH HOSPITAL · PI Francisco E Robles · 2022 to 2026
$1.9M
NIGMS NIH HHS R35 GM147437
6 · The paper itself

Abstract

BACKGROUND

aimsCell therapy is a promising treatment method that uses living cells to address a variety of diseases and conditions, including cardiovascular diseases, neurologic disorders and certain cancers. As interest in cell therapy grows, there is a need to shift to a more efficient, scalable and automated manufacturing process that can produce high-quality products at a lower cost.

methodsOne way to achieve this is using non-invasive imaging and real-time image analysis techniques to monitor and control the manufacturing process. This work presents a machine learning-based image analysis pipeline that includes semantic segmentation and anomaly detection capabilities. RESULTS/

conclusionsThis method can be easily implemented even when given a limited dataset of annotated images, is able to segment cells and debris and can identify anomalies such as contamination or hardware failure.

Indexed as

Machine LearningSemanticsImage Processing, Computer-Assistedanomaly detectioncell therapy manufacturingmachine learningsemantic segmentation

Identifiers

PMID37725031
PMCPMC10719834

What OpenQuestion holds

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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.