Abstract
We proposed a method of label-free segmentation of cell nuclei by exploiting a deep learning (DL) framework. Over the years, fluorescent proteins and staining agents have been widely used to identify cell nuclei. However, the use of exogenous agents inevitably prevents from long-term imaging of live cells and rapid analysis and even interferes with intrinsic physiological conditions. Without any agents, the proposed method was applied to label-free optical diffraction tomography (ODT) of human breast cancer cells. A novel architecture with optimized training strategies was validated through cross-modality and cross-laboratory experiments. The nucleus volumes from the DL-based label-free ODT segmentation accurately agreed with those from fluorescent-based. Furthermore, the 4D cell nucleus segmentation was successfully performed for the time-lapse ODT images. The proposed method would bring out broad and immediate biomedical applications with our framework publicly available.
Highlights
The precise localisation and segmentation of cell nucleus are crucial to understand the cell physiology in cell biology and to diagnose a malignant tumour in histopathology
The trained 2D segmentation capability of OS-Net was rigorously evaluated by cross-modality and crosslaboratory validations based on simultaneous Optical diffraction tomography (ODT) and 3D fluorescence imaging
A detailed description of each step of OS-Net is presented in Materials and Methods
Summary
The precise localisation and segmentation of cell nucleus are crucial to understand the cell physiology in cell biology and to diagnose a malignant tumour in histopathology. In addition to its primary biological function as the carrier of genetic information, the characteristics of cell nuclei play a variety of roles in medicine from diagnostics to therapeutics. The volume ratio of the nucleus to the cytoplasm is a well-established indicator of cell malignancy[1]. Light scattering spectroscopy techniques for non-invasive cancer diagnosis are known to be closely related to this nucleus-based diagnostic marker[2, 3].
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