Show simple item record

dc.contributor.authorIglesias-Rey, Sara
dc.contributor.authorAntunes-Santos, Felipe
dc.contributor.authorHagemann, Cathleen
dc.contributor.authorGomez-Cabrero, David
dc.contributor.authorBustince, Humberto
dc.contributor.authorPatani, Rickie
dc.contributor.authorSerio, Andrea
dc.contributor.authorDe Baets, Bernard
dc.contributor.authorLopez-Molina, Carlos
dc.date.accessioned2021-07-13T14:00:12Z
dc.date.available2021-07-13T14:00:12Z
dc.date.issued2021-04-21
dc.date.submitted2021-03-14
dc.identifier.citationIglesias-Rey, S., Antunes-Santos, F., Hagemann, C., Gómez-Cabrero, D., Bustince, H., Patani, R., … Lopez-Molina, C. (2021). Unsupervised Cell Segmentation and Labelling in Neural Tissue Images. Applied Sciences, 11(9), 3733. doi:10.3390/app11093733
dc.identifier.issn2076-3417
dc.identifier.doi10.3390/app11093733
dc.identifier.urihttp://hdl.handle.net/10754/670185
dc.description.abstractNeurodegenerative diseases are a group of largely incurable disorders characterised by the progressive loss of neurons and for which often the molecular mechanisms are poorly understood. To bridge this gap, researchers employ a range of techniques. A very prominent and useful technique adopted across many different fields is imaging and the analysis of histopathological and fluorescent label tissue samples. Although image acquisition has been efficiently automated recently, automated analysis still presents a bottleneck. Although various methods have been developed to automate this task, they tend to make use of single-purpose machine learning models that require extensive training, imposing a significant workload on the experts and introducing variability in the analysis. Moreover, these methods are impractical to audit and adapt, as their internal parameters are difficult to interpret and change. Here, we present a novel unsupervised automated schema for object segmentation of images, exemplified on a dataset of tissue images. Our schema does not require training data, can be fully audited and is based on a series of understandable biological decisions. In order to evaluate and validate our schema, we compared it with a state-of-the-art automated segmentation method for post-mortem tissues of ALS patients.
dc.description.sponsorshipThe authors gratefully acknowledge the financial support of the Spanish Ministry of Science (Project PID2019-108392GB-I00 AEI/FEDER, UE), the funding from the European Union’s H2020 research and innovation programme under Marie Sklodowska-Curie Grant Agreement Number 801586, as well as that of Navarra de Servicios y Tecnologías, S.A. (NASERTIC). A.S. and C.H. wish to acknowledge the support of King’s College London (Studentship “LAMBDA: long axons for motor neurons in a bioengineered model of ALS” from FoDOCS) and the Wellcome Trust (213949/Z/18/Z). On behalf of A.S., C.H. and R.P., this research was funded in whole, or in part, by the Wellcome Trust (213949/Z/18/Z). For the purposes of open access, the authorhas applied a CC BY public copyright licence to any author-accepted manuscript version arising from this submission.
dc.publisherMDPI AG
dc.relation.urlhttps://www.mdpi.com/2076-3417/11/9/3733
dc.rightsThis article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectneurodegenerative diseases
dc.subjectmedical imaging
dc.subjectobject segmentation
dc.subjectbinary image
dc.subjectimage processing
dc.subjectamyotrophic lateral sclerosis
dc.titleUnsupervised Cell Segmentation and Labelling in Neural Tissue Images
dc.typeArticle
dc.contributor.departmentBiological and Environmental Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955, Saudi Arabia.
dc.identifier.journalAPPLIED SCIENCES-BASEL
dc.identifier.wosutWOS:000649927900001
dc.eprint.versionPublisher's Version/PDF
dc.contributor.institutionDepartment of Estadistica, Informatica y Matematicas, Universidad Publica de Navarra, 31006, Pamplona, Spain;
dc.contributor.institutionNavarraBiomed, Complejo Hospitalario de Navarra, 31008, Pamplona, Spain;
dc.contributor.institutionKERMIT, Department of Data Analysis and Mathematical Modelling, Ghent University, 9000, Ghent, Belgium;
dc.contributor.institutionThe Francis Crick Institute, 1 Midland Road, London, NW1 1AT, UK;
dc.contributor.institutionCentre for Craniofacial & Regenerative Biology, King’s College London, London, WC2R 2LS, UK
dc.contributor.institutionDepartment of Neuromuscular Diseases, UCL Queen Square Institute of Neurology, Queen Square, London, WC1N 3BG, UK
dc.identifier.volume11
dc.identifier.issue9
dc.identifier.pages3733
kaust.personGomez-Cabrero, David
dc.date.accepted2021-04-18
dc.identifier.eid2-s2.0-85105202020
refterms.dateFOA2021-07-13T14:01:31Z


Files in this item

Thumbnail
Name:
applsci-11-03733-v2.pdf
Size:
10.42Mb
Format:
PDF
Description:
Publisher's version

This item appears in the following Collection(s)

Show simple item record

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.
Except where otherwise noted, this item's license is described as This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.