Nuclei instance segmentation in colon histology images with YOLOv7

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dc.authorid0000-0003-2645-8071en_US
dc.authorscopusid55807729600en_US
dc.authorwosidDGZ-5701-2022en_US
dc.contributor.authorYıldız, Serdar
dc.contributor.authorMemiş, Abbas
dc.contributor.authorVarlı, Songül
dc.date.accessioned2024-05-02T07:55:09Z
dc.date.available2024-05-02T07:55:09Z
dc.date.issued2024en_US
dc.departmentFakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Yazılım Mühendisliği Bölümüen_US
dc.description.abstractIn histology image analysis, instance-based nuclei segmentation is one of the challenging tasks within the segmentation-guided studies since it is quite troublesome to detect each distinct nuclei instance of each nuclei type in images in contrast to the semantic segmentation in which all the image pixels of a nuclei type are labelled with the same mask ID although the segmented region may comprise of multiple instances. In this paper, an instance-based medical image segmentation task is addressed, and in this context, instances of multiple types of nuclei in colon histology images are aimed to be delineated distinctly. For the instance-based segmentation of the nuclei in colon histology images, the YOLOv7 algorithm and its built-in instance segmentation module are utilized. In the experimental studies performed on Colon Nuclei Identification and Counting (CoNIC) Challenge 2022 colon histology image dataset by using a 5-fold cross-validation performance evaluation strategy, nuclei instances belonging to 6 classes as the neutrophil, epithelial, lymphocyte, plasma, eosinophil and connective were segmented. To calculate the overall system accuracy, the quantification metrics of mean average precision (mAP) and mean panoptic quality (mPQ) were measured. In performance evaluations, quite promising accuracy values were obtained. The mAP values of 0.2885 and 0.2903, and mPQ values of 0.1659 and 0.1704 were observed by using the YOLOv7 algorithm. To the best of our knowledge, this is the first nuclei instance segmentation study with YOLOv7.en_US
dc.identifier.citationYıldız, S., Memiş, A. & Varlı, S. (2023). Nuclei instance segmentation in colon histology images with YOLOv7. Ortis, A., Hameed, A.A., & Jamil, A. (Eds.) Advanced Engineering, Technology and Applications (ICAETA 2023). Communications in Computer and Information Science, 1983, pp. 335-343. Springer, Cham. https://doi.org/10.1007/978-3-031-50920-9_26en_US
dc.identifier.doi10.1007/978-3-031-50920-9_26en_US
dc.identifier.endpage343en_US
dc.identifier.isbn9783031509193
dc.identifier.isbn9783031509209
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage335en_US
dc.identifier.urihttps://doi.org/10.1007/978-3-031-50920-9_26
dc.identifier.urihttps://hdl.handle.net/20.500.13055/694
dc.identifier.volume1983en_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorMemiş, Abbas
dc.language.isoenen_US
dc.publisherSpringer Natureen_US
dc.relation.ispartofAdvanced Engineering, Technology and Applications (ICAETA 2023)en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectColon Histology Imagesen_US
dc.subjectConic 2022 Dataseten_US
dc.subjectNuclei İnstance Segmentationen_US
dc.subjectNuclei Segmentationen_US
dc.subjectYolov7en_US
dc.titleNuclei instance segmentation in colon histology images with YOLOv7en_US
dc.typeConference Objecten_US
dspace.entity.typePublication

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