This is the official implementation for: Topological-Preserving Membrane Skeleton Segmentation in Multiplex Immunofluorescence Imaging.
Source code repository
https://github.com/MASILab/MxIF_Membrane_Segmentation
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Description
Multiplex immunofluorescence (MxIF) is an emerging imaging technology whose downstream molecular analytics highly rely upon the effectiveness of cell segmentation. In practice, multiple membrane markers (e.g., NaKATPase, PanCK and β-catenin) are employed to stain membranes for different cell types, so as to achieve a more comprehensive cell segmentation since no single marker fits all cell types. However, prevalent watershed-based image processing might yield inferior capability for modeling complicated relationships between markers.
The main contributions of this work are: (1) We develop and comprehensively evaluate the deep membrane segmentation frameworks for large-scale MxIF multi-channel data. (2) We propose a novel metric that is topology preserving and skeleton-based, denoted as 𝑐𝑙𝐷𝑖𝑐𝑒𝑆𝐾𝐸𝐿 to fill the gap of lacking objective metrics for membrane skeleton segmentation. (3) We perform the first deep learning membrane segmentation study with large-scale MxIF markers.
Reference
Topological-Preserving Membrane Skeleton Segmentation in Multiplex Immunofluorescence Imaging
Shunxing Bao, Can Cui, Jia Li, Yucheng Tang, Ho Hin Lee, Ruining Deng, Lucas W Remedios, Xin Yu, Qi Yang, Sophie Chiron, Nathan Heath Patterson, Ken S Lau, Qi Liu, Joseph T Roland, Lori A Coburn, Keith T Wilson, Bennett A Landman, Yuankai Huo
SPIE Medical Imaging 2023