Cell Detection by Functional Inverse Diffusion and
Nonnegative Group Sparsity – Joakim Jaldén

On August 28, 2018, a Stockholm-based biotech company launched a new product: The Mabtech IRIS: a next-generation FluoroSpot and ELISpot reader. The reader is a machine designed to analyze biomedical image-based assays that are commonly used in immunology to study cell responses. A contemporary use case involves the development of vaccines for SARS-CoV-2 or the study of T-cells in the immune system. A core technology of the overall solution is a positivity constrained groups sparsity regularized least squares optimization problem, solved with large-scale convex optimization methods.

The presentation will outline the problem of analyzing FluoroSpot assays from a signal processing and optimization perspective and explain the methods we designed to solve it. The problem essentially amounts to counting, localizing, and quantifying heterogeneous diffuse spots in an image. The solution involves the development of a tractable linear model of the physical properties that govern the reaction-diffusion-adsorption-desorption process in the assay; the formulation of an inverse problem in function spaces and its discretized approximation; the role of group sparsity in finding a plausible solution to an otherwise ill-posed problem; and how to efficiently solve the resulting 40 million variable optimization problem on a GPU.