White blood cell detection, classification and analysis using phase imaging with computational specificity (PICS) Michae J. Fanous, Shenghua He, Sourya Sengupta, Krishnarao Tangella, Nahil Sobh, et al. Scientific Reports, 2022 Treatment of blood smears with Wright’s stain is one of the most helpful tools in detecting white blood cell abnormalities. However, to diagnose leukocyte disorders, a clinical pathologist must perform a tedious, manual process of locating and identifying individual cells. Furthermore, the staining procedure requires considerable preparation time and clinical infrastructure, which is incompatible with point-of-care diagnosis. Thus, rapid and automated evaluations of unlabeled blood smears are highly desirable. In this study, we used color spatial light interference microcopy (cSLIM), a highly sensitive quantitative phase imaging (QPI) technique, coupled with deep learning tools, to localize, classify and segment white blood cells (WBCs) in blood smears. The concept of combining QPI label-free data with AI for the purpose of extracting cellular specificity has recently been introduced in the context of fluorescence imaging as phase imaging with computational specificity (PICS). We employed AI models to first translate SLIM images into brightfield micrographs, then ran parallel tasks of locating and labelling cells using EfficientNet, which is an object detection model. Next, WBC binary masks were created using U-net, a convolutional neural network that performs precise segmentation. After training on digitally stained brightfield images of blood smears with WBCs, we achieved a mean average precision of 75% for localizing and classifying neutrophils, eosinophils, lymphocytes, and monocytes, and an average pixel-wise majority-voting F1 score of 80% for determining the cell class from semantic segmentation maps. Therefore, PICS renders and analyzes synthetically stained blood smears rapidly, at a reduced cost of sample preparation, providing quantitative clinical information.
Live-dead assay on unlabeled cells using phase imaging with computational specificity Chenfei Hu, Shenghua He, Young Jae Lee, Yuchen He, Edward M. Kong, et al. Nature Communications, 2022 Existing approaches to evaluate cell viability involve cell staining with chemical reagents. However, the step of exogenous staining makes these methods undesirable for rapid, nondestructive, and long-term investigation. Here, we present an instantaneous viability assessment of unlabeled cells using phase imaging with computation specificity. This concept utilizes deep learning techniques to compute viability markers associated with the specimen measured by label-free quantitative phase imaging. Demonstrated on different live cell cultures, the proposed method reports approximately 95% accuracy in identifying live and dead cells. The evolution of the cell dry mass and nucleus area for the labeled and unlabeled populations reveal that the chemical reagents decrease viability. The nondestructive approach presented here may find a broad range of applications, from monitoring the production of biopharmaceuticals to assessing the effectiveness of cancer treatments.
Learning-Based Cancer Treatment Outcome Prognosis Using Multimodal Biomarkers Maliazurina Saad, Shenghua He, Wade Thorstad, Hiram Gay, Daniel Barnett, et al. IEEE Transactions on Radiation and Plasma Medical Sciences, 2022 Predicting early in treatment whether a tumor is likely to be responsive is a difficult yet important task to support clinical decision making. Studies have shown that multimodal biomarkers could provide complementary information and lead to more accurate treatment outcome prognosis than unimodal biomarkers. However, the prognosis accuracy could be affected by multimodal data heterogeneity and incompleteness. The small-sized and imbalance datasets also bring additional challenges for training a designed prognosis model. In this study, a modular framework employing multimodal biomarkers for cancer treatment outcome prediction was proposed. It includes four modules of synthetic data generation, deep feature extraction, multimodal feature fusion, and classification to address the challenges described above. The feasibility and advantages of the designed framework were demonstrated through an example study, in which the goal was to stratify oropharyngeal squamous cell carcinoma (OPSCC) patients with low and high risks of treatment failures by use of the positron emission tomography (PET) image data and microRNA (miRNA) biomarkers. The superior prognosis performance and the comparison with other methods demonstrated the efficiency of the proposed framework and its ability of enabling seamless integration, validation, and comparison of various algorithms in each module of the framework. The limitation and future work were discussed as well.
A novel systematic approach for cancer treatment prognosis and its applications in oropharyngeal cancer with microRNA biomarkers Shenghua He, Chunfeng Lian, Wade Thorstad, Hiram Gay, Yujie Zhao, et al. Bioinformatics, 2021 Motivation Predicting early in treatment whether a tumor is likely to respond to treatment is one of the most difficult yet important tasks in providing personalized cancer care. Most oropharyngeal squamous cell carcinoma (OPSCC) patients receive standard cancer therapy. However, the treatment outcomes vary significantly and are difficult to predict. Multiple studies indicate that microRNAs (miRNAs) are promising cancer biomarkers for the prognosis of oropharyngeal cancer. The reliable and efficient use of miRNAs for patient stratification and treatment outcome prognosis is still a very challenging task, mainly due to the relatively high dimensionality of miRNAs compared to the small number of observation sets; the redundancy, irrelevancy and uncertainty in the large amount of miRNAs; and the imbalanced observation patient samples. Results In this study, a new machine learning-based prognosis model was proposed to stratify subsets of OPSCC patients with low and high risks for treatment failure. The model cascaded a two-stage prognostic biomarker selection method and an evidential K-nearest neighbors classifier to address the challenges and improve the accuracy of patient stratification. The model has been evaluated on miRNA expression profiling of 150 oropharyngeal tumors by use of overall survival and disease-specific survival as the end points of disease treatment outcomes, respectively. The proposed method showed superior performance compared to other advanced machine-learning methods in terms of common performance quantification metrics. The proposed prognosis model can be employed as a supporting tool to identify patients who are likely to fail standard therapy and potentially benefit from alternative targeted treatments. Availability and implementation: Code is available in https://github.com/shenghh2015/mRMR-BFT-outcome-prediction.
Deep learning-based multi-class COVID-19 classification with x-ray images Zong Fan, Su Ruan, Xiaowei Wang, Hua Li, Shenghua He Progress in Biomedical Optics and Imaging Proceedings of SPIE, 2021 The COVID-19 pandemic continues spreading rapidly around the world and has caused devastating outcomes towards the health of the global population. The reverse transcription-polymerase chain reaction (RT-PCR) test, as the only current gold standard for screening infected cases, yields a relatively high false positive rate and low sensitivity on asymptomatic subjects. The use of chest X-ray radiography (CXR) images coupled with deep- learning (DL) methods for image classification, represents an attractive adjunct to or replacement for RT-PCR testing. However, its usage has been widely debated over the past few months and its potential effectiveness remains unclear. A number of DL-based methods have been proposed to classify the COVID-19 cases from the normal ones, achieving satisfying high performance. However, these methods show limited performance on the multi-class classification task for COVID-19, pneumonia and normal cases, mainly due to two factors: 1) the textures in COVID-19 CXR images are extremely similar to that of pneumonia cases, and 2) there are much fewer COVID-19 cases compared to the other two classes in the public domain. To address these challenges, a novel framework is proposed to learn a deep convolutional neural network (DCNN) model for accurately classifying COVID-19 and pneumonia cases from other normal cases by the use of CXR images. In addition to training the model by use of conventional classification loss which measures classification accuracy, the proposed method innovatively employs a reconstruction loss measuring image fidelity and an adversarial loss measuring class distribution fidelity to assist in the training of the main DCNN model to extract more informative features to support multi-class classification. The experiment results on a COVID-19 dataset demonstrate the superior classification performance of the proposed method in terms of accuracy compared to other existing DL-based methods. The experiment on another cancer dataset further implies the potential of applying the proposed methods in other medical imaging applications.
High-resolution transcriptional and morphogenetic profiling of cells from micropatterned human esc gastruloid cultures Kyaw Thu Minn, Yuheng C Fu, Shenghua He, Sabine Dietmann, Steven C George, et al. Elife, 2020 During mammalian gastrulation, germ layers arise and are shaped into the body plan while extraembryonic layers sustain the embryo. Human embryonic stem cells, cultured with BMP4 on extracellular matrix micro-discs, reproducibly differentiate into gastruloids, expressing markers of germ layers and extraembryonic cells in radial arrangement. Using single-cell RNA sequencing and cross-species comparisons with mouse, cynomolgus monkey gastrulae, and post-implantation human embryos, we reveal that gastruloids contain cells transcriptionally similar to epiblast, ectoderm, mesoderm, endoderm, primordial germ cells, trophectoderm, and amnion. Upon gastruloid dissociation, single cells reseeded onto micro-discs were motile and aggregated with the same but segregated from distinct cell types. Ectodermal cells segregated from endodermal and extraembryonic but mixed with mesodermal cells. Our work demonstrates that the gastruloid system models primate-specific features of embryogenesis, and that gastruloid cells exhibit evolutionarily conserved sorting behaviors. This work generates a resource for transcriptomes of human extraembryonic and embryonic germ layers differentiated in a stereotyped arrangement.
