Detection of centroblast cells in H&E stained whole slide image based on object detection
4
Issued Date
2024-01-01
Resource Type
eISSN
2296858X
Scopus ID
2-s2.0-85185472549
Journal Title
Frontiers in Medicine
Volume
11
Rights Holder(s)
SCOPUS
Bibliographic Citation
Frontiers in Medicine Vol.11 (2024)
Suggested Citation
Yuenyong S., Boonsakan P., Sripodok S., Thuwajit P., Charngkaew K., Pongpaibul A., Angkathunyakul N., Hnoohom N., Thuwajit C. Detection of centroblast cells in H&E stained whole slide image based on object detection. Frontiers in Medicine Vol.11 (2024). doi:10.3389/fmed.2024.1303982 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/97405
Title
Detection of centroblast cells in H&E stained whole slide image based on object detection
Corresponding Author(s)
Other Contributor(s)
Abstract
Introduction: Detection and counting of Centroblast cells (CB) in hematoxylin & eosin (H&E) stained whole slide image (WSI) is an important workflow in grading Lymphoma. Each high power field (HPF) patch of a WSI is inspected for the number of CB cells and compared with the World Health Organization (WHO) guideline that organizes lymphoma into 3 grades. Spotting and counting CBs is time-consuming and labor intensive. Moreover, there is often disagreement between different readers, and even a single reader may not be able to perform consistently due to many factors. Method: We propose an artificial intelligence system that can scan patches from a WSI and detect CBs automatically. The AI system works on the principle of object detection, where the CB is the single class of object of interest. We trained the AI model on 1,669 example instances of CBs that originate from WSI of 5 different patients. The data was split 80%/20% for training and validation respectively. Result: The best performance was from YOLOv5x6 model that used the preprocessed CB dataset achieved precision of 0.808, recall of 0.776, mAP at 0.5 IoU of 0.800 and overall mAP of 0.647. Discussion: The results show that centroblast cells can be detected in WSI with relatively high precision and recall.