Evidence map›Paper›PMID 37243949›Full record

ArticleScience China. Life sciences2023

C1M2: a universal algorithm for 3D instance segmentation, annotation, and quantification of irregular cells.

Hao Zheng, Songlin Huang, Jing Zhang, Ren Zhang, Jialu Wang, Jing Yuan, Anan Li, Xin Yang, Zhihong Zhang

Abstract read
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In one paragraph

Article in Science China. Life sciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.4field-weighted citation impact, top 24% of its field
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

1 citing paper in PubMed, 1 citations in OpenAlex.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 2 institutions in 1 country.

Hao Zheng *Britton Chance Center and MOE Key Laboratory for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, 430074, China.
Songlin Huang *Britton Chance Center and MOE Key Laboratory for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, 430074, China.
Jing ZhangBritton Chance Center and MOE Key Laboratory for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, 430074, China.
Ren ZhangBritton Chance Center and MOE Key Laboratory for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, 430074, China.
Jialu WangBritton Chance Center and MOE Key Laboratory for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, 430074, China.
Jing YuanBritton Chance Center and MOE Key Laboratory for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, 430074, China.
Anan LiBritton Chance Center and MOE Key Laboratory for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, 430074, China.
Xin YangSchool of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, 430074, China. xinyang2014@hust.edu.cn.
Zhihong ZhangBritton Chance Center and MOE Key Laboratory for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, 430074, China. czyzzh@mail.hust.edu.cn.
Wuhan National Laboratory for Optoelectronics · CNHuazhong University of Science and Technology · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cell instance segmentation is a fundamental task for many biological applications, especially for packed cells in three-dimensional (3D) microscope images that can fully display cellular morphology. Image processing algorithms based on neural networks and feature engineering have enabled great progress in two-dimensional (2D) instance segmentation. However, current methods cannot achieve high segmentation accuracy for irregular cells in 3D images. In this study, we introduce a universal, morphology-based 3D instance segmentation algorithm called Crop Once Merge Twice (C1M2), which can segment cells from a wide range of image types and does not require nucleus images. C1M2 can be extended to quantify the fluorescence intensity of fluorescent proteins and antibodies and automatically annotate their expression levels in individual cells. Our results suggest that C1M2 can serve as a tissue cytometry for 3D histopathological assays by quantifying fluorescence intensity with spatial localization and morphological information.

Indexed as

AlgorithmsImage Processing, Computer-AssistedImaging, Three-DimensionalNeural Networks, Computer3D instance segmentationfluorescence imagesfluorescence intensityirregular cellsneural networkstissue cytometry

Identifiers

PMID37243949
OpenAlexW4378530206

What Socratic holds

Textmetadata
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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 Socratic graph.