Effective Transfer of Tumor Annotations From Hematoxylin and Eosin to Fluorescence Images of Breast and Lung Tissues

Authors

• Tianling Niu, Marquette University
• Emi Ampo, Medical College of Wisconsin
• Julie M. Jorns, Medical College of Wisconsin
• Mollie Patton, Medical College of Wisconsin
• Tongtong Lu, University of Wisconsin - Oshkosh
• Dong Hye Ye, Georgia State University
• Tina Yen, Medical College of Wisconsin
• Bing Yu, Marquette UniversityFollow

Document Type

Article

Publication Date

12-2025

Publisher

Society of Photo-optical Instrumentation Engineers (SPIE)

Source Publication

Journal of Biomedical Optics

Source ISSN

1083-3668

Original Item ID

DOI: 10.1117/1.JBO.31.1.016501

Abstract

Significance: Accurate transfer of annotations from histological images to fluorescence images is essential in developing deep learning (DL)-based optical imaging systems for intraoperative assessment of tumor margins. Manual annotation is time-consuming, prone to interobserver variability, and impractical for large-scale datasets.

Aim: We present a semi-automated method that can effectively transfer tumor annotations from pathologist-annotated hematoxylin and eosin (H&E) images to fluorescence images captured using microscopy with ultraviolet surface excitation (MUSE). This method is not intended for intraoperative use but rather to facilitate the creation of annotated datasets for DL model development.

Approach: Our semi-automated method consists of nonrigid image registration, outline extraction and refinement, and annotation transfer. The method was applied to H&E and MUSE image pairs from 35 breast and lung tissue samples. Manual annotations in MUSE images were used as the ground truth for evaluation.

Results: The proposed method achieved a Dice score coefficient of 0.87±0.07, convolutional-neural-network-based feature similarity of 0.94±0.04, and a normalized Hausdorff distance of 0.15±0.06 across the dataset.

Conclusion: These results demonstrate that the method provides a fast and accurate solution for generating annotated MUSE datasets necessary for training DL algorithms for intraoperative tumor margin detection.

Comments

Published version. Journal of Biomedical Optics, Vol. 31, No. 1 (2025): 016501. DOI. © The Authors. 

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Recommended Citation

Niu, Tianling; Ampo, Emi; Jorns, Julie M.; Patton, Mollie; Lu, Tongtong; Ye, Dong Hye; Yen, Tina; and Yu, Bing, "Effective Transfer of Tumor Annotations From Hematoxylin and Eosin to Fluorescence Images of Breast and Lung Tissues" (2025). Biomedical Engineering Faculty Research and Publications. 712.
https://epublications.marquette.edu/bioengin_fac/712