Presentation + Paper
6 April 2023 Topological-preserving membrane skeleton segmentation in multiplex immunofluorescence imaging
Author Affiliations +
Abstract
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. For example, some markers can be misleading due to questionable stain quality. In this paper, we propose a deep learning based membrane segmentation method to aggregate complementary information that is uniquely provided by large scale MxIF markers. We aim to segment tubular membrane structure in MxIF data using global (membrane markers z-stack projection image) and local (separate individual markers) information to maximize topology preservation with deep learning. Specifically, we investigate the feasibility of four SOTA 2D deep networks and four volumetric-based loss functions. We conducted a comprehensive ablation study to assess the sensitivity of the proposed method with various combinations of input channels. Beyond using adjusted rand index (ARI) as the evaluation metric, which was inspired by the clDice, we propose a novel volumetric metric that is specific for skeletal structure, denoted asclDiceSKEL. In total, 80 membrane MxIF images were manually traced for 5-fold cross-validation. Our model outperforms the baseline with a 20.2% and 41.3% increase in clDiceSKEL and ARI performance, which is significant (p<0.05) using the Wilcoxon signed rank test. Our work explores a promising direction for advancing MxIF imaging cell segmentation with deep learning membrane segmentation. Tools are available at https://github.com/MASILab/MxIF_Membrane_Segmentation.
Conference Presentation
© (2023) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
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, and Yuankai Huo "Topological-preserving membrane skeleton segmentation in multiplex immunofluorescence imaging", Proc. SPIE 12471, Medical Imaging 2023: Digital and Computational Pathology, 124710B (6 April 2023); https://doi.org/10.1117/12.2654087
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KEYWORDS
Image segmentation

Deep learning

Education and training

Biological imaging

Tissues

Cross validation

Diseases and disorders

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