Discrete Inverse Rendering: Biological Data Analysis with Integer Programming

Biological image analysis is full of discrete decisions: whether an object is present, which of several overlapping detections is real, whether two detections match across time, and whether a cell divides. Standard pipelines resolve them locally with non-max suppression, thresholding, or greedy linking, committing before all image and temporal evidence is in. We recast such problems as discrete inverse rendering: candidate renderings are generated then jointly selected to reconstruct the movie subject to temporal and biological constraints, solved to certified optimality with a modern integer-programming solver. The same formulation covers suppression of overlapping detections, selection of a structure as a path, and event-structured tracking with birth, death, and division. Applied to C. elegans splines, sperm flagella, and dividing cells, the method matches specialised state-of-the-art pipelines across three imaging modalities on a single objective, with the largest gains where per-frame segmentation is unreliable (on a low-signal fluorescence movie of Huh7 hepatoma cells, detection F1 doubles from 0.31 to 0.58). HighlightsO_LIInteger-programming framework for suppression, path, and lineage selection C_LIO_LIOne objective, one solver: worm splines, sperm flagella, and dividing cells C_LIO_LIReconstruction-based scoring matches specialised pipelines across three modalities C_LIO_LICertified-optimal solutions in seconds to minutes on standard benchmark movies C_LI In BriefZdyb and Kirkegaard recast several biological image-analysis problems--non-max suppression, structural path extraction, and event-structured cell tracking--as one discrete inverse-rendering problem solved by an integer-programming solver, matching specialised trained pipelines across three distinct imaging modalities within a single objective.