Evidence map›Paper›PMID 37856022›Full record

ArticleMethods in molecular biology (Clifton, N.J.)2024

Airway Cells 3D Reconstruction via Manual and Machine-Learning Aided Segmentation of Volume EM Datasets.

Aaran Vijayakumaran, Analle Abuammar, Odara Medagedara, Kedar Narayan, Vito Mennella

Abstract read
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In one paragraph

Article in Methods in molecular biology (Clifton, N.J.), 2024. 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
3.9field-weighted citation impact, top 6% 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, 3 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

5 authors at 3 institutions in 2 countries.

Aaran VijayakumaranMedical Research Council Toxicology Unit, School of Biological Sciences, University of Cambridge, Cambridge, UK.
Analle AbuammarMedical Research Council Toxicology Unit, School of Biological Sciences, University of Cambridge, Cambridge, UK.
Odara MedagedaraMedical Research Council Toxicology Unit, School of Biological Sciences, University of Cambridge, Cambridge, UK.
Kedar NarayanCenter for Molecular Microscopy, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, MD, USA.
Vito MennellaMedical Research Council Toxicology Unit, School of Biological Sciences, University of Cambridge, Cambridge, UK. vm430@cam.ac.uk.
Medical Research Council · GBUniversity of Cambridge · GBCenter for Cancer Research · US

Funding

Medical Research Council MC_UU_00025/13
6 · The paper itself

Abstract

Volume electron microscopy (vEM) is a high-resolution imaging technique capable of revealing the 3D structure of cells, tissues, and model organisms. This imaging modality is gaining prominence due to its ability to provide a comprehensive view of cells at the nanometer scale. The visualization and quantitative analysis of individual subcellular structures however requires segmentation of each 2D electron micrograph slice of the 3D vEM dataset; this process is extremely laborious de facto limiting its applications and throughput. To address these limitations, deep learning approaches have been recently developed including Empanada-Napari plugin, an open-source tool for automated segmentation based on a Panoptic-DeepLab (PDL) architecture. In this chapter, we provide a step-by-step protocol describing the process of manual segmentation using 3dMOD within the IMOD package and the process of automated segmentation using Empanada-Napari plugins for the 3D reconstruction of airway cellular structures.

Indexed as

Imaging, Three-DimensionalVolume Electron MicroscopyImage Processing, Computer-AssistedMachine LearningThorax3D segmentationAdvanced imagingMachine-learningNanoscale architectureVolumetric electron microscopy

Identifiers

PMID37856022
OpenAlexW4387765103

What OpenQuestion holds

Textmetadata
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.