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Segmentation of phase contrast images in multi epitope ligand cartography (MELC) for image quantification at the single cell level
Mívalt, Filip ; Taschner-Mandl,, Sabine (oponent) ; Mehnen, Lars (vedoucí práce)
The Multi-Epitope Ligand Cartography (MELC) technique enables microscopy-based visualisation of multiple cellular compartments by using immunofluorescence stains. A MELC data processing pipeline as previously established in-house within an ongoing research project, providing biologists with a tool for quantitative antibody signal analysis. The pipeline, therefore, allows phenotype characterisation of cells present in bone marrow aspirates from neuroblastoma patients. The antibody signal assignment to the plasma membrane of single cells is based on nuclear segmentation and region growing, but only approximates the real cellular shape. This approach is particularly error-prone when applied on touching or overlapping cells due to an ambiguous assignment of a single antibody signal to multiple cells. This error, subsequently, propagates to single-cell level features, thereby possibly influences ensuing phenotype classification or quantification. Hence, the segmentation of phase contrast images acquired simultaneously with each fluorescence image and visualising the whole cell (including cytoplasm and nucleus), is required to provide the pipeline with accurate segmentation masks representing the entire cell. We implemented an automated strategy employing a Mask R-CNN network to segment these phase contrast images. The algorithm achieved an overall object-level F1 score of 0.935 and a pixel-level F1 score of 0.868 when training with only a small annotated dataset. The trained model was implemented into the existing MELC data processing pipeline. Moreover, we provide an annotated dataset comprising 54 phase contrast images of bone marrow cytospin preparations containing an overall number of 1,940 cells. The implemented Mask RCNN model enables to study single cell-level features using segmentation masks representing cells predicted from phase contrast images and therefore improves an automated quantitative analysis of bone marrow samples for children’s cancer research.
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