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An Optimization Approach to Scanning Skin Direct Immunofluorescence Specimens

We propose an optimization framework for developing a fully automated scanning system. The framework allows us to have effective design choices in developing the system as every choice should be based on optimizing the objective function. We apply this framework in developing a fully automated scanning system for skin Direct Immunofluorescence (DIF) test. To that end, we introduce both non-algorithmic and algorithmic methods to optimize the objective function. Whilst the non-algorithmic methods comprise various design choices that could indirectly optimize the framework, the algorithmic methods primarily aim to optimize the objective by computing an optimal scan plan. In this work, we explore two algorithmic methods: (1) a heuristic sliding region approach and (2) a quad-tree approach. To our knowledge, this is one of the first works to describe a fully automated scanning system for skin DIF tests. As such, we propose a novel dataset that is hoped to stimulate the research interest in developing digitizing systems for skin DIF tests. All the described methods were evaluated on this novel dataset. Our scanning system is now part of a digital pathology system which has been fully deployed and routinely used within a pathology laboratory.