# B-Line Detection and Localization in Lung Ultrasound Videos Using Spatiotemporal Attention
## 2 Related Work  
Being an artefact-focused application, LUS has historically received far less attention than other ultrasound applications that focus on anatomical imaging. However, precisely because human interpretation is difficult, and because LUS relies in the analysis of image patterns, advances in deep learning which have achieved state-of-the-art performance in many image detection and classification tasks [13] can be very useful for LUS. The recent COVID-19 pandemic has motivated a significant increase in lung imaging research, and most existing work in automated LUS analysis is related to COVID-19. Related work can be broadly categorized in either detection of artefacts, such as B-lines or the localisation and segmentation of lung lesions. The first category involves techniques that address the classification of LUS frames into B-line frames or not. Although most lung imaging studies use CT [14] and X-ray [15] images, recently some studies applied ultrasound imaging to assist the automatic detection of lung artefacts. For example, [16] employed a convolutional neural network (CNN) classifier on LUS images with B-lines of different etiologies to accurately distinguish COVID-19 from similar pathology, reporting an \(F1\) score of 0.8, 0.75 and 0.81 for COVID, no COVID and HPE, respectively. Another study [6] proposed a method for the classification and weakly-supervised localization of LUS B-lines in COVID-19 patients, to an accuracy in-vivo of 0.83 to 0.89. Abnormalities in LUS images were recognised using a fully convolutional network followed by class activation maps (CAMs) [17] to generate a weakly-supervised segmentation of the images. Differently, a spatial transformer network was used by [18] for weakly-supervised localization of COVID-19 related artefacts, reporting a maximum \(F1\) score of 0.71. Further, they predicted the presence of COVID-19 related artefacts and disease severity using an ordinal regression. [PERSON] et al. [19] employed a single-shot CNN to predict bounding boxes for B-line artefacts, reporting a sensitivity/specificity of 0.88/0.93. Because all the above techniques apply the processing task in individual frames, they require prior retrieval of the relevant frame to be processed, either manually or using a method to extract candidate frames. Applying the processing task to every single frame to process an entire clip can be very sensitive to false positives. In our previous work [11], of which this paper is an extension, we proposed a temporal attention-based convolutional+LSTM model capable of detecting the B-line artefacts and localizing them within LUS videos, allowing us to exploit spatiotemporal features to classify the video and leveraging the temporal attention mechanism to identify the frame where the most salient features are. Figure 2: The proposed architecture for LUS B-line detection and spatiotemporal localization. This model consists of a spatial feature extraction module (CNN layers), followed by a spatial attention network, then a bidirectional LSTM, and a temporal attention module. The parameters of each layer and module are detailed in the text. The second category focuses on localising the image lesion, by using attention mechanisms and segmentation methods. Most of this work was carried out on lung images acquired from other modalities such as CT and X-ray. [PERSON] et al. [14] proposed a residual attention U-Net for the multi-class segmentation of chest CT images from COVID-19 patients. A similar approach was employed for X-ray lung segmentation of pneumonia [15]. A 3D CNN network with online attention refinement and a dual-sampling strategy was developed by [20] to differentiate COVID-19 artefacts from pneumonia in chest CT images. [PERSON] et al. [21] proposed a lesion-attention deep neural network (LA-DNN) that performs binary classification of the presence of COVID-19 and, in addition, five lesion classification with multi-label attention. [PERSON] et al.