The Role of Pleura and Adipose in Lung Ultrasound AI

Learning the Imaging Landmarks: Unsupervised Key point Detection in Lung Ultrasound Videos

Lung ultrasound (LUS) is an increasingly popular diagnostic imaging modality for continuous and periodic monitoring of lung infection, given its advantages of non-invasiveness, non-ionizing nature, portability and easy disinfection. The major landmarks assessed by clinicians for triaging using LUS are pleura, A and B lines. There have been many efforts for the automatic detection of these landmarks. However, restricting to a few pre-defined landmarks may not reveal the actual imaging biomarkers particularly in case of new pathologies like COVID-19. Rather, the identification of key landmarks s…

肺与心外膜脂肪组织影像组学特征在检测COVID-19感染严重程度中的增量价值与可解释性

Incremental Value and Interpretability of Radiomics Features of Both Lung and Epicardial Adipose Tissue for Detecting the Severity of COVID-19 Infection

提出三阶段深度学习提取EAT并融合肺与EAT影像组学特征的混合模型，在515例COVID-19患者中显著提升严重程度检测性能与可解释性。

ZAGNet：用于患者级肺部超声诊断的区域感知图聚合网络

ZAGNet: Zone-Aware Graph Aggregation Network for Patient-Level Lung Ultrasound Diagnosis

ZAGNet利用图神经网络建模肺部超声区域间关系，处理缺失区域，在714名受试者数据集上，实变AUC达0.803，胸腔积液AUC达0.893，较池化方法提升显著。

基于小波和Radon变换的肺部超声图像自动特征检测

Automatic Feature Detection in Lung Ultrasound Images using Wavelet and Radon Transforms

本研究提出基于小波和Radon变换的方法，自动检测肺部超声图像中的胸膜线、A线和B线，在200张真实图像上分别达到100%、86%和62%的F2分数，验证了其有效性。

An Approach Towards Physics Informed Lung Ultrasound Image Scoring Neural Network for Diagnostic Assistance in COVID-19

Ultrasound is fast becoming an inevitable diagnostic tool for regular and continuous monitoring of the lung with the recent outbreak of COVID-19. In this work, a novel approach is presented to extract acoustic propagation-based features to automatically highlight the region below pleura, which is an important landmark in lung ultrasound (LUS). Subsequently, a multichannel input formed by using the acoustic physics-based feature maps is fused to train a neural network, referred to as LUSNet, to classify the LUS images into five classes of varying severity of lung infection to track the progress…

Dense Pixel-Labeling for Reverse-Transfer and Diagnostic Learning on Lung Ultrasound for COVID-19 and Pneumonia Detection

We propose using a pre-trained segmentation model to perform diagnostic classification in order to achieve better generalization and interpretability, terming the technique reverse-transfer learning. We present an architecture to convert segmentation models to classification models. We compare and contrast dense vs sparse segmentation labeling and study its impact on diagnostic classification. We compare the performance of U-Net trained with dense and sparse labels to segment A-lines, B-lines, and Pleural lines on a custom dataset of lung ultrasound scans from 4 patients. Our experiments show …