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Deep Neural Networks, Image Analysis, Supervised Learning, Cell Size, Jaccard Index, Intersection Over Union, Balanced Accuracy
Abstract:
High throughput microscopy imaging yields vast amount of image data, e.g. in microbiology, cell biology, and medical diagnostics calling for automated analysis methods. Despite recent progress in employing deep neural networks to image segmentation in a supervised learning setting, these models often do not meet the performance requirement when used without model refinement in particular when cells accumulate and overlap in the image plane. Here, we analyse segmentation performance gains obtained through retraining and through transfer learning using a curated dataset of phase contrast microscopy images taken of individual cells and cell accumulations of Pseudomonas fluorescens SBW25. Both methods yield significant improvement over the baseline model DeLTA2 (O’Conner et al. PLOS Comp. Biol 18, e1009797 (2022)) in intersection–over–union and balanced accuracy test metrics. We demonstrate that (computationally cheaper) transfer learning of only 25% of neural network layers yields the same improvement over the baseline as a complete retraining run. Furthermore, we achieve highest performance boosts when the training data contains only well separated cells even though the test split may contain cell accumulations. This opens up the possibility for a semi–automated segmentation workflow combining feature extraction techniques for ground truth mask generation from low complexity images and supervised learning for the more complex data.
(Online presentation)
