Evidence map›Paper›PMID 40120179›Full record

ArticleComputers in biology and medicine2025

SegElegans: Instance segmentation using dual convolutional recurrent neural network decoder in Caenorhabditis elegans microscopic images.

Pablo E Layana Castro, Konstantinos Kounakis, Antonio García Garví, Ilias Gkikas, Ioannis Tsiamantas, Nektarios Tavernarakis, Antonio-José Sánchez-Salmerón

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Article in Computers in biology and medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
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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

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

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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

7 authors.

Pablo E Layana CastroUniversitat Politècnica de Valéncia, Instituto de Automática e Informática Industrial, Camino de Vera S/n, Edificio 8G Acceso D, Valencia, 46022, Valencia, Spain. Electronic address: pablacas@doctor.upv.es.
Konstantinos KounakisDepartment of Basic Sciences, Faculty of Medicine, University of Crete, Heraklion, 71110, Crete, Greece; Institute of Molecular Biology and Biotechnology, Foundation for Research and Technology-Hellas, Heraklion, 71110, Crete, Greece. Electronic address: kostas_kounakis@imbb.forth.gr.
Antonio García GarvíUniversitat Politècnica de Valéncia, Instituto de Automática e Informática Industrial, Camino de Vera S/n, Edificio 8G Acceso D, Valencia, 46022, Valencia, Spain. Electronic address: angar25a@upv.edu.es.
Ilias GkikasInstitute of Molecular Biology and Biotechnology, Foundation for Research and Technology-Hellas, Heraklion, 71110, Crete, Greece; Department of Biology, School of Sciences and Engineering, University of Crete, Heraklion, 71110, Crete, Greece. Electronic address: igkikas@imbb.forth.gr.
Ioannis TsiamantasInstitute of Molecular Biology and Biotechnology, Foundation for Research and Technology-Hellas, Heraklion, 71110, Crete, Greece; Department of Biology, School of Sciences and Engineering, University of Crete, Heraklion, 71110, Crete, Greece. Electronic address: ioan.tsiamantas@gmail.com.
Nektarios TavernarakisDepartment of Basic Sciences, Faculty of Medicine, University of Crete, Heraklion, 71110, Crete, Greece; Institute of Molecular Biology and Biotechnology, Foundation for Research and Technology-Hellas, Heraklion, 71110, Crete, Greece. Electronic address: tavernarakis@imbb.forth.gr.
Antonio-José Sánchez-SalmerónUniversitat Politècnica de Valéncia, Instituto de Automática e Informática Industrial, Camino de Vera S/n, Edificio 8G Acceso D, Valencia, 46022, Valencia, Spain. Electronic address: asanchez@isa.upv.es.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Caenorhabditis elegans is a great model for exploring organismal, cellular, and subcellular biology through optical and fluorescence microscopy, with its research applications steadily expanding. However, manual processing of numerous microscopic images is prone to errors and demands significant labor due to worms tendency to touch or cluster with each other. Here, we present a new system for segmenting whole-body instances of Caenorhabditis elegans in microscopic images (referred to as SegElegans), employing a combination of neural network architecture and conventional image processing techniques. Our method effectively overcomes previous challenges and resolves many instances of contact and overlap between worms in highly populated images in a timely manner. The results obtained show an average Intersection over Union value of 96.3% per worm and an average improvement of 6% over other existing methods for automated analysis of worm images. SegElegns is a user-friendly application for Caenorhabditis elegans segmentation that will benefit whole-worm phenotypic screenings essential for studying development, behavior, aging, and disease.

Indexed as

Caenorhabditis elegansImage Processing, Computer-AssistedMicroscopyNeural Networks, ComputerAnimalsRecurrent Neural NetworksAutomated analysisCaenorhabditis elegansMicroscopic imagesNeural networkSegmentationSkeletonizing

Identifiers

PMID40120179
PMCPMC12048300

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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.