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Uterine electromyography (uEMG) measures the electrical activity of the uterus noninvasively and is a promising technique for detecting preterm birth. Nevertheless, uterine contractions are irregular during pregnancy and may not present during standard 30-min recording. Hence, this study analyzes the noncontraction of uEMG signals for predicting premature birth. Three channels of 53 and 47 noncontraction segments under the term and preterm conditions, respectively, are obtained from the publicly available database. The signals are preprocessed, and the contractions and noncontraction segments are extracted manually based on the annotations. The Hjorth features, namely activity, mobility, and complexity, are extracted from the signals. Classification algorithms, namely support vector machine, random forest, and adaptive boosting classifier, are designed to distinguish between term and preterm conditions. The results show that mobility decreases, and complexity increases in preterm conditions. The support vector machine based on the proposed features of a single channel yields a maximum accuracy of 84.3% and F1-score of 82.8% in differentiating term and preterm conditions. In order to improve the performance further, we adapted a decision fusion approach that combines predictions from multiple channels. The improved model enhances the accuracy and F1-score by about 3%. Therefore, it appears that the proposed approach using noncontraction segments could be used as a biomarker for the reliable prediction of premature birth.

In this work, an attempt is made to investigate the association of geometric changes in mediastinum and lungs with Coronavirus Disease-2019 (COVID-19) using chest radiographic images. For this, the normal and COVID-19 images are considered from a public database. Reaction-diffusion level set is employed to segment the lung fields. Further, Chan Vese level set mechanism is used to delineate the mediastinum. Features, such as area, convex area, and bounding box area, are extracted from the mediastinum and lung masks. Then, mediastinum to lungs ratiometric features are derived, and statistical analysis is performed. The results demonstrate that the proposed methods are able to segment both regions by capturing significant anatomical landmarks. The ratiometric indices, along with mediastinum measures, are observed to be statistically significant for normal and COVID-19 conditions. Mediastinum convex area for COVID-19 conditions is found to be two times greater than normal subjects indicating the maximum difference in values between the classes. An AUC of 94% is obtained using SVM classifier for differentiating normal and COVID-19 conditions. Thus, the investigation of the mechanics of structural alterations of lungs and mediastinum is significant in COVID-19 diagnosis. As the proposed approach is able to detect COVID-19 conditions, it could act as a decision support system to assist clinicians in early detection.

Cell painting technique provides large amount of potential information for applications such as drug discovery, bioactivity prediction and cytotoxicity assessment. However, its utility is restricted due to the requirement of advanced, costly and specific instrumentation protocols. Therefore, creating cell painted images using simple microscopic data can provide a better alternative for these applications. This study investigates the applicability of deep network-based semantic segmentation to generate cell painted images of nuclei, endoplasmic reticulum (ER) and cytoplasm from a composite image. For this, 3456 composite images from a public dataset of Broad Bioimage Benchmark collection are considered. The corresponding ground truth images for nuclei, ER and cytoplasm are generated using Otsu’s thresholding technique and used as labeled dataset. Semantic segmentation network is applied to these data and optimized using stochastic gradient descent with momentum algorithm at a learning rate of 0.01. The segmentation performance of the trained network is evaluated using accuracy, loss, mean Boundary [Formula: see text] (BF) score, Dice Index, Jaccard Index and structural similarity index. Gradient weighted Class Activation Mapping (Grad-CAM) is employed to visualize significant image regions identified by the model. Further, a cellular index is proposed as a geometrical measure which is capable of differentiating the segmented cell organelles. The trained model yields 96.52% accuracy with a loss of 0.07 for 50 epochs. Dice Index of 0.93, 0.76 and 0.75 is achieved for nuclei, ER and cytoplasm respectively. It is observed that nuclei to cytoplasm provides comparatively higher percentage change (74.56%) in the ratiometric index than nuclei to ER and ER to cytoplasm. The achieved results demonstrate that the proposed study can predict the cell painted organelles from a composite image with good performance measures. This study could be employed for generating cell painted organelles from raw microscopy images without using specific fluorescent labeling.

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