title: Cell segmentation in phase contrast microscopy images via semi-supervised classification over optics-related features.
abstract: 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.
authors.affiliation: Department of Electronic Engineering, Shanghai Jiaotong University, China; The Robotics Institute, Carnegie Mellon University, USA. suhangss@gmail.com
authors.forename: Hang
authors.initials: H
authors.lastname: Su
authors.forename: Zhaozheng
authors.initials: Z
authors.lastname: Yin
authors.forename: Seungil
authors.initials: S
authors.lastname: Huh
authors.forename: Takeo
authors.initials: T
authors.lastname: Kanade
mesh_terms: D000465:Algorithms; D016477:Artifacts; D001185:Artificial Intelligence; D058948:Cell Tracking / Q000379:methods*; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008858:Microscopy, Phase-Contrast / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
keywords: Cell segmentation; Phase contrast microscopy image; Phase retardation feature; Semi-supervised classification; Sparse representation