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https://hdl.handle.net/20.500.13091/726
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DC Field | Value | Language |
---|---|---|
dc.contributor.author | İnik, Özkan | - |
dc.contributor.author | Ceyhan, Ayşe | - |
dc.contributor.author | Balcıoğlu, Esra | - |
dc.contributor.author | Ülker, Erkan | - |
dc.date.accessioned | 2021-12-13T10:29:52Z | - |
dc.date.available | 2021-12-13T10:29:52Z | - |
dc.date.issued | 2019 | - |
dc.identifier.issn | 0010-4825 | - |
dc.identifier.issn | 1879-0534 | - |
dc.identifier.uri | https://doi.org/10.1016/j.compbiomed.2019.103350 | - |
dc.identifier.uri | https://hdl.handle.net/20.500.13091/726 | - |
dc.description.abstract | The ovary is a complex endocrine organ that shows significant structural and functional changes in the female reproductive system over recurrent cycles. There are different types of follicles in the ovarian tissue. The reproductive potential of each individual depends on the numbers of these follicles. However, genetic mutations, toxins, and some specific drugs have an effect on follicles. To determine these effects, it is of great importance to count the follicles. The number of follicles in the ovary is usually counted manually by experts, which is a tedious, time-consuming and intense process. In some cases, the experts count the follicles in a subjective way due to their knowledge. In this study, for the first time, a method has been proposed for automatically counting the follicles of ovarian tissue. Our method primarily involves filter-based segmentation applied to whole slide histological images, based on a convolutional neural network (CNN). A new method is also proposed to eliminate the noise that occurs after the segmentation process and to determine the boundaries of the follicles. Finally, the follicles whose boundaries are determined are classified. To evaluate its performance, the results of the proposed method were compared with those obtained by two different experts and the results of the Faster R-CNN model. The number of follicles obtained by the proposed method was very close to the number of follicles counted by the experts. It was also found that the proposed method was much more successful than the Faster R-CNN model. | en_US |
dc.description.sponsorship | Scientific and Technological Research Council of Turkey (TUBITAK)Turkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK) [2180141] | en_US |
dc.description.sponsorship | This study has been supported by the Scientific and Technological Research Council of Turkey (TUBITAK), funded by the 1512 - Entrepreneurship Multi-Phase Program with project number 2180141. | en_US |
dc.language.iso | en | en_US |
dc.publisher | PERGAMON-ELSEVIER SCIENCE LTD | en_US |
dc.relation.ispartof | COMPUTERS IN BIOLOGY AND MEDICINE | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | Deep Learning | en_US |
dc.subject | Cnn | en_US |
dc.subject | Faster R-Cnn | en_US |
dc.subject | Ovary | en_US |
dc.subject | Follicle Count | en_US |
dc.subject | Segmentation | en_US |
dc.subject | Classification | en_US |
dc.subject | Number | en_US |
dc.subject | Classification | en_US |
dc.subject | Accurate | en_US |
dc.subject | Nuclei | en_US |
dc.title | A new method for automatic counting of ovarian follicles on whole slide histological images based on convolutional neural network | en_US |
dc.type | Article | en_US |
dc.identifier.doi | 10.1016/j.compbiomed.2019.103350 | - |
dc.identifier.pmid | PubMed: 31330319 | en_US |
dc.identifier.scopus | 2-s2.0-85069610303 | en_US |
dc.department | Fakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü | en_US |
dc.authorid | Ulker, Erkan/0000-0003-4393-9870 | - |
dc.authorwosid | Ulker, Erkan/ABA-5846-2020 | - |
dc.authorwosid | inik, ozkan/AAX-1578-2021 | - |
dc.identifier.volume | 112 | en_US |
dc.identifier.wos | WOS:000487566700001 | en_US |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
dc.authorscopusid | 57210128271 | - |
dc.authorscopusid | 57201153284 | - |
dc.authorscopusid | 35739614100 | - |
dc.authorscopusid | 23393979800 | - |
dc.identifier.scopusquality | Q2 | - |
item.languageiso639-1 | en | - |
item.fulltext | With Fulltext | - |
item.cerifentitytype | Publications | - |
item.openairetype | Article | - |
item.grantfulltext | embargo_20300101 | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
crisitem.author.dept | 02.03. Department of Computer Engineering | - |
Appears in Collections: | Mühendislik ve Doğa Bilimleri Fakültesi Koleksiyonu PubMed İndeksli Yayınlar Koleksiyonu / PubMed Indexed Publications Collections Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collections WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collections |
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