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Inpainting Missing Tissue in Multiplexed Immunofluorescence Imaging

Posted by on Friday, December 10, 2021 in Abdomen Imaging, Big Data, Crohn's disease, Deep Learning, Generative Adversarial Networks, Histology, Image Processing, Image Segmentation, Reproducibility.

Shunxing Bao, Yucheng Tang, Ho Hin Lee, Riqiang Gao, Qi Yang, Xin Yu, Sophie Chiron, Lori A. Coburn, Keith T. Wilson, Joseph T. Roland, Bennett A. Landman, Yuankai Huo. “Inpainting Missing Tissue in Multiplexed Immunofluorescence Imaging” Medical Imaging 2022: Digital and Computational Pathology. International Society for Optics and Photonics, accepted

Full text: [TBD]


Multiplex immunofluorescence (MxIF) is an emerging technique that allows for staining multiple cellular and histological markers to stain simultaneously on a single tissue section. However, with multiple rounds of staining and bleaching, it is inevitable that the scarce tissue may be physically depleted. Thus, a digital way of synthesizing such missing tissue would be appealing since it would increase the useable areas for the downstream single-cell analysis. In this work, we investigate the feasibility of employing generative adversarial network (GAN) approaches to synthesize missing tissues using 11 MxIF structural molecular markers (i.e., epithelial and stromal). Briefly, we integrate a multi-channel high-resolution image synthesis approach to synthesize the missing tissue from the remaining markers. The performance of different methods is quantitatively evaluated via the downstream cell membrane segmentation task. Our contribution is that we, for the first time, assess the feasibility of synthesizing missing tissues in MxIF via quantitative segmentation. The proposed synthesis method has comparable reproducibility with the baseline method on performance for the missing tissue region reconstruction only, but it improves 40% on whole tissue synthesis that is crucial for practical application. We conclude that GANs are a promising direction of advancing MxIF imaging with deep image synthesis.


MxIF Inpainting

Figure 3. Workflow of the PixNto1-MT to inpaint the missing tissue regions by aggregating the information from residual tissues and information from other channels. Only four markers are shown for illustration. Specifically, the biochemical mask library provides noise applied to the target channel and generates simulated stains with missing tissue. The generator aims to synthesis the whole target image patch instead of the missing area only.