Whole Slide Image Annotation Support for Estimating Lesion Proportions

• Published in: Proceedings (International Conference on Cyberworlds. Online) pp. 356 - 357
• Main Authors: Takano, Nana, Nishiguchi, Satoshi, Toyoura, Masahiro
• Format: Conference Proceeding
• Language: English
• Published: IEEE 29.10.2024
• Subjects: Accuracy; annotation; Annotations; Biomedical imaging; Image annotation; Image segmentation; learning from label proportions (LLP); Lesions; Whole Slide Image (WSI)
• ISSN: 2642-3596
• DOI: 10.1109/CW64301.2024.00071

The annotation of medical images for the purpose of segmentation demands a high level of expertise and experience, and the generation of suitable datasets represents a significant challenge. In this study, we propose a method that facilitates annotation on a whole slide image (WSI) without requiring detailed specification of the lesion area. The objective is to enable annotation without the need for specialised knowledge. In particular, the WSI is divided into smaller regions for annotation purposes, with the proportion of lesions in each region being compared with reference images of similar regions, for which the proportion of lesions is known. The proportion of lesions in the reference small region image deemed to be most analogous is then designated as the annotation. The outcomes of an annotation experiment on multiple subjects based on this approach indicated that as the level of experience of the annotator increased, the variability in the accuracy rate diminished. This suggests that the annotators had acquired knowledge and skills through the process of annotation.