TY - GEN
T1 - Combining Multiple Ground Truth Annotations for Segmentation Training for Oral Cavity Cancer
AU - Folmsbee, Jonathan
AU - Brandwein-Weber, Margaret
AU - Doyle, Scott
N1 - Publisher Copyright:
© 2023 SPIE.
PY - 2023
Y1 - 2023
N2 - Annotation of true ground truth is a difficult task for many computational pathology problems. Types of ground truth labels in the field include bounding boxes, text labels, binary class labels, and full tissue maps. The compounding issue is when multiple different pathologists label the same image, and there is disagreement between them. In this work, we investigate multiply reannotated tumor maps for squamous cell carcinoma, and if different annotation fusion methods have an impact on tumor segmentation. We find in this work that tumor label maps with an average annotation similarity of 0.759, do not have a significant quantitative difference in tumor segmentation.
AB - Annotation of true ground truth is a difficult task for many computational pathology problems. Types of ground truth labels in the field include bounding boxes, text labels, binary class labels, and full tissue maps. The compounding issue is when multiple different pathologists label the same image, and there is disagreement between them. In this work, we investigate multiply reannotated tumor maps for squamous cell carcinoma, and if different annotation fusion methods have an impact on tumor segmentation. We find in this work that tumor label maps with an average annotation similarity of 0.759, do not have a significant quantitative difference in tumor segmentation.
UR - https://www.scopus.com/pages/publications/85160566578
U2 - 10.1117/12.2654301
DO - 10.1117/12.2654301
M3 - Conference contribution
AN - SCOPUS:85160566578
T3 - Progress in Biomedical Optics and Imaging - Proceedings of SPIE
BT - Medical Imaging 2023
A2 - Tomaszewski, John E.
A2 - Ward, Aaron D.
PB - SPIE
T2 - Medical Imaging 2023: Digital and Computational Pathology
Y2 - 19 February 2023 through 23 February 2023
ER -