Cell segmentation methods for label-free contrast microscopy: review and comprehensive comparison

Abstract

Background: Because of its non-destructive nature, label-free imaging is an important strategy for studying biological processes. However, routine microscopic techniques like phase contrast or DIC suffer from shadow-cast artifacts making automatic segmentation challenging. The aim of this study was to compare the segmentation efficacy of published steps of segmentation work-flow (image reconstruction, foreground segmentation, cell detection (seed-point extraction) and cell (instance) segmentation) on a dataset of the same cells from multiple contrast microscopic modalities.

Results: We built a collection of routines aimed at image segmentation of viable adherent cells grown on the culture dish acquired by phase contrast, differential interference contrast, Hoffman modulation contrast and quantitative phase imaging, and we performed a comprehensive comparison of available segmentation methods applicable for label-free data. We demonstrated that it is crucial to perform the image reconstruction step, enabling the use of segmentation methods originally not applicable on label-free images. Further we compared foreground segmentation methods (thresholding, feature-extraction, level-set, graph-cut, learning-based), seed-point extraction methods (Laplacian of Gaussians, radial symmetry and distance transform, iterative radial voting, maximally stable extremal region and learning-based) and single cell segmentation methods. We validated suitable set of methods for each microscopy modality and published them online.

Conclusions: We demonstrate that image reconstruction step allows the use of segmentation methods not originally intended for label-free imaging. In addition to the comprehensive comparison of methods, raw and reconstructed annotated data and Matlab codes are provided.

Keywords: Cell segmentation; Differential contrast image; Image reconstruction; Laplacian of Gaussians; Methods comparison; Microscopy; Quantitative phase imaging.

Fig. 1

Block diagram showing segmentation approach. For details of individual steps, see Results and Materials and Methods.EGT, empirical gradient treshold; LoG, Laplacian of Gaussians, DT, distance transform, MSER maximally stable extremal region

Fig. 2

Quality of reconstructions a. field of view for raw and reconstructed HMC, DIC, PC and QPI images. Image width is 375 μm and 85 μm for field of view and detail below (b). receiver operator curve for particular image reconstruction (c). profile of reconstructed image corresponding to section in detail in (a). AUC, area under curve, ROC, receiver-operator curve

Fig. 3

Foreground-background segmentation step. a representative images showing tested foreground-background segmentation methods of rDIC-Koos-reconstructed DIC image. Dependency between area used for training and Dice coefficient for learning-based approach Ilastik (b) and Weka (c). scalebar indicates 50 μm

Fig. 4

Seed-point extraction segmentation step and all-in-one segmentation approaches. a Results of segmentation, representative image of rDIC-Koos-reconstructed DIC image followed by foreground-background segmentation with Traniable Weka Segmentation. Blue points indicate seeds based on which cells are segmented using marker-controlled watershed. Note absence of seed-points for “all-in-one” segmentation approaches. b Dependency between number of cells used for training and Dice coefficient for Celldetect

Fig. 5

Cell segmentation efficacy and cell morphology. a histograms showing distribution of circularity and level of contact with other cells (shown as percentage of cell perimeter touching with other cells. Based on histograms, low/high circularity and isolated/growing together groups were created. b effect of cell reconstruction, on segmentation accuracy, subset of low/high circularity and low/high contact with other cells (for this step, dLoGm-Kong was used in next segmentation step for all methods). c effect of various Seed-point extraction methods, effect of low/high circularity and low/high contact on segmentation efficacy. Last step is shown for QPI data only