A convolutional neural network model for semantic segmentation of mitotic events in microscopy images

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dc.contributor.authorOrturk, Saban
dc.contributor.authorAkdemir, Bayram
dc.date.accessioned2020-03-26T20:12:17Z
dc.date.available2020-03-26T20:12:17Z
dc.date.issued2019
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractMitosis, which has important effects such as healing and growing for human body, has attracted considerable attention in recent years. Especially, cell division characteristics contain useful information for regenerative medicine. However, the analysis of this complex structure is very challenging process for experts, because many cells are scattered at random times and at different speeds. Therefore, we propose an automatic mitosis event detection method using convolutional neural network (CNN). In the proposed method, semantic segmentation has been applied with the help of CNN in order to make the complex mitosis images more easily understandable. The CNN structure consists of four convolution layers, four pooling layers, one rectified linear unit layer and softmax layer. Generally, the aim of CNN structure is to reduce the image size, but in this study, the image size is preserved for the semantic segmentation which provides high-level information. For this, the size of the images at each layer output is calculated and updated with the appropriate padding parameters. Thus, real-size images presented at the network output can be easily understood. BAEC and C2C12 phase-contrast microscopy image sequences are used for experiments. The precision, recall and F-score parameters are used for evaluating the success of the proposed method and compared with the other methods using the same datasets.en_US
dc.identifier.doi10.1007/s00521-017-3333-9en_US
dc.identifier.endpage3728en_US
dc.identifier.issn0941-0643en_US
dc.identifier.issn1433-3058en_US
dc.identifier.issue8en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage3719en_US
dc.identifier.urihttps://dx.doi.org/10.1007/s00521-017-3333-9
dc.identifier.urihttps://hdl.handle.net/20.500.12395/37408
dc.identifier.volume31en_US
dc.identifier.wosWOS:000485922300035en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSPRINGER LONDON LTDen_US
dc.relation.ispartofNEURAL COMPUTING & APPLICATIONSen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectSemantic segmentationen_US
dc.subjectConvolutional neural networken_US
dc.subjectCNNen_US
dc.subjectMitosis segmentationen_US
dc.titleA convolutional neural network model for semantic segmentation of mitotic events in microscopy imagesen_US
dc.typeArticleen_US

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