Combining multiple ground truth annotations for segmentation training for oral cavity cancer

Jonathan Folmsbee; Margaret Brandwein-Weber; Scott Doyle

doi:10.1117/12.2654301

6 April 2023 Combining multiple ground truth annotations for segmentation training for oral cavity cancer

Jonathan Folmsbee, Margaret Brandwein-Weber, Scott Doyle

Proceedings Volume 12471, Medical Imaging 2023: Digital and Computational Pathology; 1247112 (2023) https://doi.org/10.1117/12.2654301
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Abstract

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.

Conference Presentation

Citation Download Citation

Jonathan Folmsbee, Margaret Brandwein-Weber, and Scott Doyle "Combining multiple ground truth annotations for segmentation training for oral cavity cancer", Proc. SPIE 12471, Medical Imaging 2023: Digital and Computational Pathology, 1247112 (6 April 2023); https://doi.org/10.1117/12.2654301

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