TY - GEN
T1 - Quality Control of Whole Slide Images using the YOLO Concept
AU - Hemmatirad, Kimia
AU - Babaie, Morteza
AU - Afshari, Mehdi
AU - Maleki, Danial
AU - Saiadi, Mahjabin
AU - Tizhoosh, Hamid R.
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Computational pathology applies computer vision algorithms on whole slide images. The digitization of tissue glass slides marks a significant change in the clinical diagnostic workflow. One of the challenges in digital pathology is the presence of artifacts such as tissue fold, air bubbles, and ink-markers on archived cases. These artifacts may affect the focus points in digital scanners, and their presence may negatively affect the quality of the output tissue image and the subsequent diagnosis. Manual review of whole slide images requires experts, and it is a laborious and time-consuming task. In this paper, we trained the YOLO-v4 (You-Only-Look-Once) model to detect air bubble edges, tissue folds, which can happen during slide preparation, and ink-marked tissue glass slides, which occur when pathologists highlight regions of interest on glass slides. Our method is not only fast but also highly accurate. The experiments showed 99.5 % IOU calculation (intersection over union, also called Jaccard Index) for locating artifacts.
AB - Computational pathology applies computer vision algorithms on whole slide images. The digitization of tissue glass slides marks a significant change in the clinical diagnostic workflow. One of the challenges in digital pathology is the presence of artifacts such as tissue fold, air bubbles, and ink-markers on archived cases. These artifacts may affect the focus points in digital scanners, and their presence may negatively affect the quality of the output tissue image and the subsequent diagnosis. Manual review of whole slide images requires experts, and it is a laborious and time-consuming task. In this paper, we trained the YOLO-v4 (You-Only-Look-Once) model to detect air bubble edges, tissue folds, which can happen during slide preparation, and ink-marked tissue glass slides, which occur when pathologists highlight regions of interest on glass slides. Our method is not only fast but also highly accurate. The experiments showed 99.5 % IOU calculation (intersection over union, also called Jaccard Index) for locating artifacts.
KW - YOLO model
KW - artifact removal
KW - digital pathology
KW - ink markers
KW - tissue folds
UR - http://www.scopus.com/inward/record.url?scp=85139011179&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85139011179&partnerID=8YFLogxK
U2 - 10.1109/ICHI54592.2022.00049
DO - 10.1109/ICHI54592.2022.00049
M3 - Conference contribution
AN - SCOPUS:85139011179
T3 - Proceedings - 2022 IEEE 10th International Conference on Healthcare Informatics, ICHI 2022
SP - 282
EP - 287
BT - Proceedings - 2022 IEEE 10th International Conference on Healthcare Informatics, ICHI 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 10th IEEE International Conference on Healthcare Informatics, ICHI 2022
Y2 - 11 June 2022 through 14 June 2022
ER -