Automated Cell Segmentation for Quantitative Phase Microscopy

Abstract

Automated cell segmentation and tracking is essential for dynamic studies of cellular morphology, movement, and interactions as well as other cellular behaviors. However, accurate, automated, and easy-to-use cell segmentation remains a challenge, especially in cases of high cell densities, where discrete boundaries are not easily discernable. Here, we present a fully automated segmentation algorithm that iteratively segments cells based on the observed distribution of optical cell volumes measured by quantitative phase microscopy. By fitting these distributions to known probability density functions, we are able to converge on volumetric thresholds that enable valid segmentation cuts. Since each threshold is determined from the observed data itself, virtually no input is needed from the user. We demonstrate the effectiveness of this approach over time using six cell types that display a range of morphologies, and evaluate these cultures over a range of confluencies. Facile dynamic measures of cell mobility and function revealed unique cellular behaviors that relate to tissue origins, state of differentiation, and real-time signaling. These will improve our understanding of multicellular communication and organization.

Quantitative Phase Dynamics of Cancer Cell Populations Affected by Blue Light

“…The analysis process consists of segmentation, the interconnection of matching cells in adjacent time frames and extraction of the analyzed dynamical and morphological cell features. For cell segmentation in each frame, Loewke's Iterative Thresholding (LIT) method [33] was combined with Empirical Gradient Thresholding (EGT) technique [34]. EGT was shown to be a very robust and parameter-free foreground cell segmentation method (semantic segmentation) across various microscopical modalities [35].…”

“…Furthermore, holes smaller than hole_area_threshold and objects [34], where the result is refined with additional thresholding and object/hole filtering. Single-cell separation uses LIT algorithm [33]. Input parameters to individual steps are shown with dashed arrows.…”

“…Finally, the object in FSM is separated by watershed lines, which are created by the application of the watershed algorithm [36] on Euclidean distance transform [37] of masks of objects created with thresholding. In original LIT [33], threshold grows until it reaches maximum image intensity, which leads to over-segmentation in some cases, thus we have introduced a maximal_LIT_threshold parameter and iterations are stopped when the threshold reaches this value.…”

Morphological Multi-Cell Discrimination for Robust Cell Segmentation

“…GFT identifies cells by clustering the slope vector using the characteristic that the slope vector becomes very congested around a cell, but in a noisy environment, the slope at the cell boundary is quite small, and the direction is unreliable. GC-based algorithms [7]- [11] are widely used because they are guaranteed to find a global optimal solution for pixel boundaries between distinct regions. However, these methods may produce boundaries of an uneven step-like shape that are different from the boundaries perceived by a human; hence, a more advanced segmentation method is required.…”

“…The active contour model (ACM) [5], [6] is a representative energy-minimization technique that generates adequate results on noisy images based on initial points defined by a user. Graph cut (GC) [7]- [11], another segmentation method based on energy minimization, finds a global optimal solution for a given initial value.…”