Cell Segmentation in Phase Contrast Microscopy Images Via Semi-Supervised Classification Over Optics-Related Features


Phase-contrast microscopy is one of the most common and convenient imaging modalities to observe long-term multi-cellular processes, which generates images by the interference of lights passing through transparent specimens and background medium with different retarded phases. Despite many years of study, computer-aided phase contrast microscopy analysis on cell behavior is challenged by image qualities and artifacts caused by phase contrast optics. Addressing the unsolved challenges, the authors propose (1) a phase contrast microscopy image restoration method that produces phase retardation features, which are intrinsic features of phase contrast microscopy, and (2) a semi-supervised learning based algorithm for cell segmentation, which is a fundamental task for various cell behavior analysis. Specifically, the image formation process of phase contrast microscopy images is first computationally modeled with a dictionary of diffraction patterns; as a result, each pixel of a phase contrast microscopy image is represented by a linear combination of the bases, which we call phase retardation features. Images are then partitioned into phase-homogeneous atoms by clustering neighboring pixels with similar phase retardation features. Consequently, cell segmentation is performed via a semi-supervised classification technique over the phase-homogeneous atoms. Experiments demonstrate that the proposed approach produces quality segmentation of individual cells and outperforms previous approaches.


Computer Science

Keywords and Phrases

Cell Segmentation; Phase Retardation; Phase-Contrast Microscopy Images; Semi-Supervised Classification; Sparse Representation; Computer Aided Analysis; Image Reconstruction; Pixels; Supervised Learning; Algorithm; Article; Cell Function; Cell Membrane; Cell Segmentation; Cellular Parameters; Computer Aided Design; Human; Image Analysis; Image Quality; Mitosis; Phase Contrast Microscopy; Priority Journal; Cell Segmentation; Phase Contrast Microscopy Image; Phase Retardation Feature; Semi-Supervised Classification; Sparse Representation; Algorithms; Artifacts; Artificial Intelligence; Cell Tracking; Image Enhancement; Image Interpretation; Computer-Assisted; Microscopy; Phase-contrast; Pattern Recognition; Automated; Reproducibility of Results; Sensitivity and Specificity

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Article - Journal

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