Instance Segmentation of Neuronal Nuclei Leveraging Domain Adaptation

Kevin Brady, Pooya Khorrami, Lars Gjesteby, Laura J. Brattain

Abstract: The detection and localization of individual cell nuclei in dense neural scenes collected by microscopy traditionally depends on human-expert-intensive manual markup for training and evaluating automatic algorithms. These approaches are expensive, time-intensive, and require domain expertise. To develop automatic approaches, the annotated content needs to match the collection conditions (e.g. stain, cell-type) and small changes to these conditions often requires additional matching annotated content. Our approach leverages supervised domain adaptation approach with application to the instance segmentation of nuclei in the brain. The efficacy of this approach is demonstrated experimentally by characterizing the performance of adapting models learned on content not well matched to the target domain. Quantitative results demonstrate performance improvements relative to previous related work. High Performance Computing (HPC) applications of this technology include Human-in-the-Loop (HIL) retraining leveraging active learning or similar machine learning approaches.