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.INDEX TERMS Cell nucleus segmentation, deep learning, label-free segmentation, optical diffraction tomography, refractive index tomogram.

The healthcare industry is in dire need of rapid microbial identification techniques for treating microbial infections. Microbial infections are a major healthcare issue worldwide, as these widespread diseases often develop into deadly symptoms. While studies have shown that an early appropriate antibiotic treatment significantly reduces the mortality of an infection, this effective treatment is difficult to practice. The main obstacle to early appropriate antibiotic treatments is the long turnaround time of the routine microbial identification, which includes time-consuming sample growth. Here, we propose a microscopy-based framework that identifies the pathogen from single to few cells. Our framework obtains and exploits the morphology of the limited sample by incorporating three-dimensional quantitative phase imaging and an artificial neural network. We demonstrate the identification of 19 bacterial species that cause bloodstream infections, achieving an accuracy of 82.5% from an individual bacterial cell or cluster. This performance, comparable to that of the gold standard mass spectroscopy under a sufficient amount of sample, underpins the effectiveness of our framework in clinical applications. Furthermore, our accuracy increases with multiple measurements, reaching 99.9% with seven different measurements of cells or clusters. We believe that our framework can serve as a beneficial advisory tool for clinicians during the initial treatment of infections.

For appropriate treatments of infectious diseases, rapid identification of the pathogens is crucial. Here, we developed a rapid and label-free method for identifying common bacterial pathogens as individual bacteria by using three-dimensional quantitative phase imaging and deep learning. We achieved 95% accuracy in classifying 19 bacterial species by exploiting the rich information in three-dimensional refractive index tomograms with a convolutional neural network classifier. Extensive analysis of the features extracted by the trained classifier was carried out, which supported that our classifier is capable of learning species-dependent characteristics. We also confirmed that utilizing three-dimensional refractive index tomograms was crucial for identification ability compared to two-dimensional imaging. This method, which does not require time-consuming culture, shows high feasibility for diagnosing patients with infectious diseases who would benefit from immediate and adequate antibiotic treatment.

In tomographic reconstruction, the image quality of the reconstructed images can be significantly degraded by defects in the measured two-dimensional (2D) raw image data. Despite the importance of screening defective 2D images for robust tomographic reconstruction, manual inspection and rule-based automation suffer from low-throughput and insufficient accuracy, respectively. Here, we present deep learning-enabled quality control for holographic data to produce robust and high-throughput optical diffraction tomography (ODT). The key idea is to distil the knowledge of an expert into a deep convolutional neural network. We built an extensive database of optical field images with clean/noisy annotations, and then trained a binary-classification network based upon the data. The trained network outperformed visual inspection by non-expert users and a widely used rule-based algorithm, with >90% test accuracy. Subsequently, we confirmed that the superior screening performance significantly improved the tomogram quality. To further confirm the trained model’s performance and generalisability, we evaluated it on unseen biological cell data obtained with a setup that was not used to generate the training dataset. Lastly, we interpreted the trained model using various visualisation techniques that provided the saliency map underlying each model inference. We envision the proposed network would a powerful lightweight module in the tomographic reconstruction pipeline.

In order to identify cell nuclei, fluorescent proteins or staining agents has been widely used. However, use of exogenous agents inevitably prevents from long-term imaging of live cells and rapid analysis, and even interferes with intrinsic physiological conditions. In this work, we proposed a method of label-free segmentation of cell nuclei in optical diffraction tomography images by exploiting a deep learning framework. The proposed method was applied for precise cell nucleus segmentation in two, three, and four-dimensional label-free imaging.A novel architecture with optimised training strategies was validated through cross-modality and cross-laboratory experiments. The proposed method would bring out broad and immediate biomedical applications with our framework publicly available.