Detection of centroblast cells in H&E stained whole slide image based on object detection

Sumeth Yuenyong; Paisarn Boonsakan; Supasan Sripodok; Peti Thuwajit; Komgrid Charngkaew; Ananya Pongpaibul; Napat Angkathunyakul; Narit Hnoohom; Chanitra Thuwajit

doi:10.3389/fmed.2024.1303982

Detection of centroblast cells in H&E stained whole slide image based on object detection

Front Med (Lausanne). 2024 Feb 7:11:1303982. doi: 10.3389/fmed.2024.1303982. eCollection 2024.

Authors

Sumeth Yuenyong¹, Paisarn Boonsakan², Supasan Sripodok³, Peti Thuwajit⁴, Komgrid Charngkaew³, Ananya Pongpaibul³, Napat Angkathunyakul³, Narit Hnoohom⁵, Chanitra Thuwajit⁴

Affiliations

¹ Department of Computer Engineering, Faculty of Engineering, Mahidol University, Nakhon Pathom, Thailand.
² Department of Pathology, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand.
³ Department of Pathology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand.
⁴ Department of Immunology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand.
⁵ Image Information and Intelligence Laboratory, Department of Computer Engineering, Faculty of Engineering, Mahidol University, Nakhon Pathom, Thailand.

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.

Keywords: H&E; artificial intelligence; centroblast; object detection; whole slide image.

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. The authors acknowledge support from the New Discovery and Frontier Research Grant (R016420005, Fund 3), Mahidol University.