Sub-micro scale cell segmentation using deep learning

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dc.authoridTIRYAKI, VOLKAN MUJDAT/0000-0003-1824-5260
dc.authoridShreiber, David/0000-0001-8248-419X
dc.contributor.authorTiryaki, Volkan Mujdat
dc.contributor.authorAyres, Virginia M.
dc.contributor.authorAhmed, Ijaz
dc.contributor.authorShreiber, David, I
dc.date.accessioned2024-12-24T19:24:08Z
dc.date.available2024-12-24T19:24:08Z
dc.date.issued2022
dc.departmentSiirt Üniversitesi
dc.description.abstractAutomated cell segmentation is key for rapid and accurate investigation of cell responses. As instrumentation resolving power increases, clear delineation of newly revealed cellular features at the submicron through nanoscale becomes important. Reliance on the manual investigation of myriad small features retards investigation; however, use of deep learning methods has great potential to reveal cell features both at high accuracy and high speed, which may lead to new discoveries in the near term. In this study, semantic cell segmentation systems were investigated by implementing fully convolutional neural networks called U-nets for the segmentation of astrocytes cultured on poly-l-lysine-functionalized planar glass. The network hyperparameters were determined by changing the number of network layers, loss functions, and input image modalities. Atomic force microscopy (AFM) images were selected for investigation as these are inherently nanoscale and are also dimensional. AFM height, deflection, and friction images were used as inputs separately and together, and the segmentation performances were investigated on five-fold cross-validation data. Transfer learning methods, including VGG16, VGG19, and Xception, were used to improve cell segmentation performance. We find that AFM height images inherit more discriminative features than AFM deflection and AFM friction images for cell segmentation. When transfer-learning methods are applied, statistically significant segmentation performance improvements are observed. Segmentation performance was compared to classical image processing algorithms and other algorithms in use by considering both AFM and electron microscopy segmentation. An accuracy of 0.9849, Matthews correlation coefficient of 0.9218, and Dice's similarity coefficient of 0.9306 were obtained on the AFM test images. Performance evaluations show that the proposed system can be successfully used for AFM cell segmentation with high precision.
dc.description.sponsorshipNational Science Foundation [ARRA-CBET-0846328, PHY0957776]; Siirt University Scientific Research Projects Directorate [2021-S_ I UMUH-01]
dc.description.sponsorshipNational Science Foundation, Grant/Award Numbers: ARRA-CBET-0846328, PHY0957776; Siirt University Scientific Research Projects Directorate, Grant/Award Number: 2021-S_ I UMUH-01
dc.identifier.doi10.1002/cyto.a.24533
dc.identifier.endpage520
dc.identifier.issn1552-4922
dc.identifier.issn1552-4930
dc.identifier.issue6
dc.identifier.pmid35000269
dc.identifier.scopus2-s2.0-85123245803
dc.identifier.scopusqualityQ1
dc.identifier.startpage507
dc.identifier.urihttps://doi.org/10.1002/cyto.a.24533
dc.identifier.urihttps://hdl.handle.net/20.500.12604/5854
dc.identifier.volume101
dc.identifier.wosWOS:000745011600001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherWiley
dc.relation.ispartofCytometry Part A
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241222
dc.subjectastrocyte
dc.subjectatomic force microscopy
dc.subjectcell culture
dc.subjectcell morphology
dc.titleSub-micro scale cell segmentation using deep learning
dc.typeArticle

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