This is the official implementation for: Alleviating tiling effect by random walk sliding window in high-resolution histological whole slide image synthesis.
Source code repository
https://github.com/MASILab/RandomWalkSlidingWindow
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Description
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 highresolution 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.
The contribution of this work is four-fold: (1) We propose a novel unpaired “set to
image” synthesis model (MxIF to H&E) that utilizes the rich structural information in 27
MxIF channels by aggregating simultaneous nuclei and mucin segmentation supervision as
spatial constraints; (2) We introduce a sliding window shifting strategy during the optimized
inference stage so as to alleviate the tiling effects; (3) 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; (4) We evaluated the synthesis model in
a quantitative manner by employing epithelial cell segmentation as a downstream task.
Reference
Alleviating tiling effect by random walk sliding window in high-resolution histological whole slide image synthesis
Shunxing Bao, Ho Hin Lee, Qi Yang, Lucas Walker Remedios, Ruining Deng, Can Cui, Leon Yichen Cai, Kaiwen Xu, Xin Yu, Sophie Chiron, Yike Li, Nathan Heath Patterson, Yaohong Wang, Jia Li, Qi Liu, Ken S. Lau, Joseph T. Roland, Lori A. Coburn, Keith T. Wilson, Bennett A. Landman, Yuankai Huo
MIDL 2023