Blind stain separation using model-aware generative learning and its applications on fluorescence microscopy images

• URL: http://arxiv.org/abs/2102.06802v1
• Date: Fri, 12 Feb 2021 22:39:39 GMT
• Title: Blind stain separation using model-aware generative learning and its applications on fluorescence microscopy images
• Authors: Xingyu Li
• Abstract summary: Prior model-based stain separation methods rely on stains' spatial distributions over an image. Deep generative models are used for this purpose. In this study, a novel learning-based blind source separation framework is proposed.
• Score: 1.713291434132985
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multiple stains are usually used to highlight biological substances in biomedical image analysis. To decompose multiple stains for co-localization quantification, blind source separation is usually performed. Prior model-based stain separation methods usually rely on stains' spatial distributions over an image and may fail to solve the co-localization problem. With the advantage of machine learning, deep generative models are used for this purpose. Since prior knowledge of imaging models is ignored in purely data-driven solutions, these methods may be sub-optimal. In this study, a novel learning-based blind source separation framework is proposed, where the physical model of biomedical imaging is incorporated to regularize the learning process. The introduced model-relevant adversarial loss couples all generators in the framework and limits the capacities of the generative models. Further more, a training algorithm is innovated for the proposed framework to avoid inter-generator confusion during learning. This paper particularly takes fluorescence unmixing in fluorescence microscopy images as an application example of the proposed framework. Qualitative and quantitative experimentation on a public fluorescence microscopy image set demonstrates the superiority of the proposed method over both prior model-based approaches and learning-based methods.

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