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 computer-aided 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 $128\times256$ two fully connected layers gave the best accuracy of 87% It is concluded that the proposed method could assess the ultrasound images by assigning LUSS automatically with high accuracy, potentially applicable to the clinics

/pdf/quantitative-analysis-and-automated-lung-ultrasound-scoring-38j33vufsh.pdf

Quantitative Analysis and Automated Lung Ultrasound Scoring for Evaluating COVID-19 Pneumonia With Neural Networks

https://doi.org/10.1016/j.bspc.2022.103561

Automated lung ultrasound scoring for evaluation of coronavirus disease 2019 pneumonia using two-stage cascaded deep learning model

An omics-to-omics joint knowledge association subtensor model for radiogenomics cross-modal modules from genomics and ultrasonic images of breast cancers

Automated Pleural Line Detection Based on Radon Transform Using Ultrasound.

Monitoring fetal growth is an essential activity throughout gestation period. It is necessary for the well-being of the mother and the child so that any abnormalities can be diagnosed at the earlier stage itself and preventive measures can be taken. MRI Image analysis is necessary to identify the anomalies that have a higher probability of occurrence during the first and second trimesters. Fetal brain MRI images are obtained and analysed for any disorder in the neurodevelopment of the embryo. Classification of the fetal brain image into normal and abnormal cases is done using deep learning algorithms like densenet121 and squeezenet1_0. Densenet produced an accuracy of 98.7% when compared to squeezenet whose accuracy is 96.3%. The performance of densenet is high in case of various metrics like precision, recall, sensitivity and F1 Score.

Performance Analysis of Squeezenet and Densenet on Fetal Brain MRI Dataset

Simultaneous Segmentation of Fetal Hearts and Lungs for Medical Ultrasound Images via an Efficient Multi-scale Model Integrated With Attention Mechanism.

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.

/pdf/ultrasonographic-segmentation-of-fetal-lung-with-deep-1v2fq05yq0.pdf

Jiangang Chen

Papers

Simultaneous Segmentation of Fetal Hearts and Lungs for Medical Ultrasound Images via an Efficient Multi-scale Model Integrated With Attention Mechanism.

Ultrasonographic Segmentation of Fetal Lung with Deep Learning

Automated Pleural Line Detection Based on Radon Transform Using Ultrasound.

Automated Ultrasound Measurement of the Inferior Vena Cava: An Animal Study.