DC pole | Wartość | Język |
dc.contributor.author | Marzec, Mariusz Paweł | - |
dc.contributor.author | Piórkowski, Adam | - |
dc.contributor.author | Gertych, Arkadiusz | - |
dc.date.accessioned | 2022-06-21T10:27:47Z | - |
dc.date.available | 2022-06-21T10:27:47Z | - |
dc.date.issued | 2022 | - |
dc.identifier.citation | BMC Bioinformatics, Vol. 23 , iss. 1 (2022), art. no. 203 | pl_PL |
dc.identifier.issn | 1471-2105 | - |
dc.identifier.uri | http://hdl.handle.net/20.500.12128/23524 | - |
dc.description.abstract | Background: High-content screening (HCS) is a pre-clinical approach for the assessment
of drug efficacy. On modern platforms, it involves fluorescent image capture
using three-dimensional (3D) scanning microscopy. Segmentation of cell nuclei in 3D
images is an essential prerequisite to quantify captured fluorescence in cells for screening.
However, this segmentation is challenging due to variabilities in cell confluency,
drug-induced alterations in cell morphology, and gradual degradation of fluorescence
with the depth of scanning. Despite advances in algorithms for segmenting nuclei for
HCS, robust 3D methods that are insensitive to these conditions are still lacking.
Results: We have developed an algorithm which first generates a 3D nuclear mask in
the original images. Next, an iterative 3D marker-controlled watershed segmentation
is applied to downsized images to segment adjacent nuclei under the mask. In the
last step, borders of segmented nuclei are adjusted in the original images based on
local nucleus and background intensities. The method was developed using a set of 10
3D images. Extensive tests on a separate set of 27 3D images containing 2,367 nuclei
demonstrated that our method, in comparison with 6 reference methods, achieved
the highest precision (PR = 0.97), recall (RE = 0.88) and F1-score (F1 = 0.93) of nuclei
detection. The Jaccard index (JI = 0.83), which reflects the accuracy of nuclei delineation,
was similar to that yielded by all reference approaches. Our method was on average
more than twice as fast as the reference method that produced the best results.
Additional tests carried out on three stacked 3D images comprising heterogenous
nuclei yielded average PR = 0.96, RE = 0.84, F1 = 0.89, and JI = 0.80.
Conclusions: The high-performance metrics yielded by the proposed approach suggest
that it can be used to reliably delineate nuclei in 3D images of monolayered and
stacked cells exposed to cytotoxic drugs. | pl_PL |
dc.language.iso | en | pl_PL |
dc.rights | Uznanie autorstwa 3.0 Polska | * |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/pl/ | * |
dc.subject | Image processing | pl_PL |
dc.subject | Image analysis | pl_PL |
dc.subject | 3D nuclei segmentation | pl_PL |
dc.subject | Automated analysis | pl_PL |
dc.subject | High-content screening | pl_PL |
dc.subject | Bio-image informatics | pl_PL |
dc.title | Efficient automatic 3D segmentation of cell nuclei for high-content screening | pl_PL |
dc.type | info:eu-repo/semantics/article | pl_PL |
dc.relation.journal | BMC Bioinformatics | pl_PL |
dc.identifier.doi | 10.1186/s12859-022-04737-4 | - |
Pojawia się w kolekcji: | Artykuły (WNŚiT)
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