Resolving complex cartilage structures in developmental biology via deep learning-based automatic segmentation of X-ray computed microtomography images

Simple item page
dc.contributor.authorMatula, Jancs
dc.contributor.authorPoláková, Veronikacs
dc.contributor.authorŠalplachta, Jakubcs
dc.contributor.authorTesařová, Markétacs
dc.contributor.authorZikmund, Tomášcs
dc.contributor.authorKaucká, Markétacs
dc.contributor.authorAdameyko, Igorcs
dc.contributor.authorKaiser, Jozefcs
dc.coverage.issue1cs
dc.coverage.volume12cs
dc.date.accessioned2022-05-25T14:52:20Z
dc.date.available2022-05-25T14:52:20Z
dc.date.issued2022-05-24cs
dc.description.abstractThe complex shape of embryonic cartilage represents a true challenge for phenotyping and basic understanding of skeletal development. X-ray computed microtomography (mu CT) enables inspecting relevant tissues in all three dimensions; however, most 3D models are still created by manual segmentation, which is a time-consuming and tedious task. In this work, we utilised a convolutional neural network (CNN) to automatically segment the most complex cartilaginous system represented by the developing nasal capsule. The main challenges of this task stem from the large size of the image data (over a thousand pixels in each dimension) and a relatively small training database, including genetically modified mouse embryos, where the phenotype of the analysed structures differs from the norm. We propose a CNN-based segmentation model optimised for the large image size that we trained using a unique manually annotated database. The segmentation model was able to segment the cartilaginous nasal capsule with a median accuracy of 84.44% (Dice coefficient). The time necessary for segmentation of new samples shortened from approximately 8 h needed for manual segmentation to mere 130 s per sample. This will greatly accelerate the throughput of mu CT analysis of cartilaginous skeletal elements in animal models of developmental diseases.en
dc.formattextcs
dc.format.extent1-13cs
dc.format.mimetypeapplication/pdfcs
dc.identifier.citationScientific Reports. 2022, vol. 12, issue 1, p. 1-13.en
dc.identifier.doi10.1038/s41598-022-12329-8cs
dc.identifier.issn2045-2322cs
dc.identifier.other177989cs
dc.identifier.urihttp://hdl.handle.net/11012/204432
dc.language.isoencs
dc.publisherNATURE PORTFOLIOcs
dc.relation.ispartofScientific Reportscs
dc.relation.urihttps://www.nature.com/articles/s41598-022-12329-8cs
dc.rightsCreative Commons Attribution 4.0 Internationalcs
dc.rights.accessopenAccesscs
dc.rights.sherpahttp://www.sherpa.ac.uk/romeo/issn/2045-2322/cs
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/cs
dc.subjectConvolutional neural networken
dc.subjectsegmentationen
dc.subjectchondrocraniumen
dc.subjectcraniofacial cartilageen
dc.subjectnasal capsuleen
dc.subjectmouse embryoen
dc.subjectX-ray computed tomographyen
dc.subjectCTen
dc.subject3D imagingen
dc.titleResolving complex cartilage structures in developmental biology via deep learning-based automatic segmentation of X-ray computed microtomography imagesen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
sync.item.dbidVAV-177989en
sync.item.dbtypeVAVen
sync.item.insts2023.02.20 12:50:56en
sync.item.modts2023.02.20 12:12:47en
thesis.grantorVysoké učení technické v Brně. Fakulta strojního inženýrství. Ústav fyzikálního inženýrstvícs
thesis.grantorVysoké učení technické v Brně. Středoevropský technologický institut VUT. Pokročilé instrumentace a metody pro charakterizace materiálůcs
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
s41598022123298.pdf
Size:
4.5 MB
Format:
Adobe Portable Document Format
Description:
s41598022123298.pdf
Download
Collections
Ústav fyzikálního inženýrství
Pokročilé instrumentace a metody pro charakterizace materiálů