As being radiation-free, portable, and capable of repetitive use, ultrasonography is playing an important role in diagnosing and evaluating the COVID-19 Pneumonia (PN) in this epidemic. By virtue of lung ultrasound scores (LUSS), lung ultrasound (LUS) was used to estimate the excessive lung fluid that is an important clinical manifestation of COVID-19 PN, with high sensitivity and specificity. However, as a qualitative method, LUSS suffered from large interobserver variations and requirement for experienced clinicians. Considering this limitation, we developed a quantitative and automatic lung ultrasound scoring system for evaluating the COVID-19 PN. A total of 1527 ultrasound images prospectively collected from 31 COVID-19 PN patients with different clinical conditions were evaluated and scored with LUSS by experienced clinicians. All images were processed via a series of computeraided analysis, including curve-to-linear conversion, pleural line detection, region-of-interest (ROI) selection, and feature extraction. A collection of 28 features extracted from the ROI was specifically defined for mimicking the LUSS. Multilayer fully connected neural networks, support vector machines, and decision trees were developed for scoring LUS images using the fivefold cross validation. The model with 128 × 256 two fully connected layers gave the best accuracy of 87%. It is concluded that the proposed method could Manuscript

The morbidity and mortality of the fetus is related closely with the neonatal respiratory morbidity, which was caused by the immaturity of the fetal lung primarily. The amniocentesis has been used in clinics to evaluate the maturity of the fetal lung, which is invasive, expensive and time-consuming. Ultrasonography has been developed to examine the fetal lung quantitatively in the past decades as a non-invasive method. However, the contour of the fetal lung required by existing studies was delineated in manual. An automated segmentation approach could not only improve the objectiveness of those studies, but also offer a quantitative way to monitor the development of the fetal lung in terms of morphological parameters based on the segmentation. In view of this, we proposed a deep learning model for automated fetal lung segmentation and measurement. The model was constructed based on the U-Net. It was trained by 3500 data sets augmented from 250 ultrasound images with both the fetal lung and heart manually delineated, and then tested on 50 ultrasound data sets. With the proposed method, the fetal lung and cardiac area were automatically segmented with the accuracy, average IoU, sensitivity and precision being 0.98, 0.79, 0.881 and 0.886, respectively.

As the second most common primary liver tumour, the early detection of cholangiocarcinoma is very important. Computer-aided diagnosis based on deep learning using pathological tissue images is often used in cancer diagnosis. Compared with traditional RGB pathological images, hyperspectral image has more advantages in deep learning based automatic pathological diagnosis because it contains spectral dimension information. In this paper, a ResNet-50 based method is used to identify cholangiocarcinoma from microscopy hyperspectral images. The microscope hyperspectral choledoch tissue images are captured by our microscopy hyperspectral imaging system (MHIS) and annotated by experienced pathologists manually. After pre-processing and data argumentation, we split them in to training set (6800 images) and testing set (210 images) and choose ResNet-50 structure to train the classification model. The classification model can automatically classify the choledich tissue images into cancerous and non-cancerous regions. Our experimental results show that the accuracy of proposed method is 82.4% in case of ResNet-50 structure.