Nuclei instance segmentation in colon histology images with YOLOv7
Kapalı Erişim
Tarih
2024
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Springer Nature
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
In 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.
Açıklama
Anahtar Kelimeler
Colon Histology Images, Conic 2022 Dataset, Nuclei İnstance Segmentation, Nuclei Segmentation, Yolov7
Kaynak
Advanced Engineering, Technology and Applications (ICAETA 2023)
WoS Q Değeri
Scopus Q Değeri
Q3
Cilt
1983
Sayı
Künye
Yıldız, 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_26
