A two-stage deep counting for bacterial colonies from multi-sources

Abstract

Automatic bacterial colony counting (BCC) from Petri dish images is full of challenges due to commonly exhibited overlapping, occlusion, and inconsistency problems. Modern deep learning methods require sufficient annotated data to identify colonies from a single source and yet usually perform badly on new sources, which makes it difficult to be extended. In this paper, a scalable two-stage deep learning framework is proposed to count bacterial colonies from multi-sources with both generic and specific features. In addition, a novel blending based strategy is proposed to augment the data and raise the robustness by increasing the sample complexity. We also suggest a refinement of the coarse results from existing BCC programmes could save a lot of time and effort for annotation. Four state-of-the-art methods are selected to compare with ours. Our method outperforms others in terms of mean average precision, averaged root-mean-square error and mean absolute percentage error. Hyper-parameters and other settings are also investigated in the experiments, which proved the scalability, robustness and accuracy of the proposed method.