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title abstract layout series publisher issn id month tex_title firstpage lastpage page order cycles bibtex_author author date address container-title volume genre issued pdf extras
Alleviating tiling effect by random walk sliding window in high-resolution histological whole slide image synthesis
Multiplex immunofluorescence (MxIF) is an advanced molecular imaging technique that can simultaneously provide biologists with multiple (i.e., more than 20) molecular markers on a single histological tissue section. Unfortunately, due to imaging restrictions, the more routinely used hematoxylin and eosin (H&E) stain is typically unavailable with MxIF on the same tissue section. As biological H&E staining is not feasible, previous efforts have been made to obtain H&E whole slide image (WSI) from MxIF via deep learning empowered virtual staining. However, the tiling effect is a long-lasting problem in high-resolution WSI-wise synthesis. The MxIF to H&E synthesis is no exception. Limited by computational resources, the cross-stain image synthesis is typically performed at the patch-level. Thus, discontinuous intensities might be visually identified along with the patch boundaries assembling all individual patches back to a WSI. In this work, we propose a deep learning based unpaired high-resolution image synthesis method to obtain virtual H&E WSIs from MxIF WSIs (each with 27 markers/stains) with reduced tiling effects. Briefly, we first extend the CycleGAN framework by adding simultaneous nuclei and mucin segmentation supervision as spatial constraints. Then, we introduce a random sliding window shifting strategy during the optimized inference stage to alleviate the tiling effects. The validation results show that our spatially constrained synthesis method achieves a 56% performance gain for the downstream cell segmentation task. The proposed inference method reduces the tiling effects by using 50% fewer computation resources without compromising performance. The proposed random sliding window inference method is a plug-and-play module, which can be generalized and used for other high-resolution WSI image synthesis applications.
inproceedings
Proceedings of Machine Learning Research
PMLR
2640-3498
bao24a
0
Alleviating tiling effect by random walk sliding window in high-resolution histological whole slide image synthesis
1406
1422
1406-1422
1406
false
Bao, Shunxing and Lee, Ho Hin and Yang, Qi and Remedios, Lucas Walker and Deng, Ruining and Cui, Can and Cai, Leon Yichen and Xu, Kaiwen and Yu, Xin and Chiron, Sophie and Li, Yike and Patterson, Nathan Heath and Wang, Yaohong and Li, Jia and Liu, Qi and Lau, Ken S. and Roland, Joseph T. and Coburn, Lori A. and Wilson, Keith T. and Landman, Bennett A. and Huo, Yuankai
given family
Shunxing
Bao
given family
Ho Hin
Lee
given family
Qi
Yang
given family
Lucas Walker
Remedios
given family
Ruining
Deng
given family
Can
Cui
given family
Leon Yichen
Cai
given family
Kaiwen
Xu
given family
Xin
Yu
given family
Sophie
Chiron
given family
Yike
Li
given family
Nathan Heath
Patterson
given family
Yaohong
Wang
given family
Jia
Li
given family
Qi
Liu
given family
Ken S.
Lau
given family
Joseph T.
Roland
given family
Lori A.
Coburn
given family
Keith T.
Wilson
given family
Bennett A.
Landman
given family
Yuankai
Huo
2024-01-23
Medical Imaging with Deep Learning
227
inproceedings
date-parts
2024
1
23