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Coronavirus Disease 2019 (COVID-19) has been spreading rapidly, threatening global health. Computer-aided screening on chest computed tomography (CT) images using deep learning, especially, lesion segmentation, is an effective complement for COVID-19 diagnosis. Although edge detection highly benefits lesion segmentation, an independent COVID-19 edge detection task in CT scans has been unprecedented and faces several difficulties, e.g., ambiguous boundaries, noises and diverse edge shapes. To this end, we propose the first COVID-19 lesion edge detection model: COVID Edge-Net, containing one edge detection backbone and two new modules: the multi-scale residual dual attention (MSRDA) module and the Canny operator module. MSRDA module helps capture richer contextual relationships for obtaining better deep learning features, which are fused with Canny features from Canny operator module to extract more accurate, refined, clearer and sharper edges. Our approach achieves the state-of-the-art performance and can be a benchmark for COVID-19 edge detection. Code related to this paper is available at: https://github.com/Elephant-123/COVID-Edge-Net.

Automatic segmentation of lung lesions in computer tomography has the potential to ease the burden of clinicians during the Covid-19 pandemic. Yet predictive deep learning models are not trusted in the clinical routine due to failing silently in out-of-distribution (OOD) data. We propose a lightweight OOD detection method that exploits the Mahalanobis distance in the feature space. The proposed approach can be seamlessly integrated into state-of-the-art segmentation pipelines without requiring changes in model architecture or training procedure, and can therefore be used to assess the suitability of pre-trained models to new data. We validate our method with a patch-based nnU-Net architecture trained with a multi-institutional dataset and find that it effectively detects samples that the model segments incorrectly.

As the most fatal cancer type, early diagnosis of the lung cancer plays an important role for the survival of the patients. Diagnosis of the lung cancer involves screening the patients initially by Computed Tomography (CT) for the presence of lung lesions. This procedure requires expert radiologists which need to go over very large numbers of image slices manually in order to detect and diagnose lung lesions. Unfortunately this is a very time consuming process and its performance is very dependent on the performing radiologist. Thus assisting the radiologists by developing an automated computer aided detection (CAD) system is an interesting research goal. In this regard, as the aim of this paper a pre-trained AlexNet (deep learning) framework is transferred to develop and implement a robust CAD system for the classification of lung images depending on whether they bear a lung lesion or not. High performances of 98.72% sensitivity, 98.35% specificity and 98.48% accuracy are reported as a result.

This section provides a comprehensive procedural report for core biopsy of lung lesion procedure with up-to-date explanatory notes, synopsis of the indications and contraindications and potential complications in an organized and practical format.

This section provides a comprehensive procedural report for core biopsy of lung lesion procedure with up-to-date explanatory notes, synopsis of the indications and contraindications, and potential complications in an organized and practical format.

This section provides a comprehensive procedural report for fine needle aspiration (FNA) of lung lesion procedure with up-to-date explanatory notes, synopsis of the indications and contraindications, and potential complications in an organized and practical format.

Air bronchograms and pseudocavitations (↑) are often visible in adenocarcinomas (bronchoalveolar carcinomas) and seldom occur in benign lung lesions. Air bronchograms can be also found in pneumonias, round atelectases, inside conglomerate masses in silicosis, and sarcoidosis. Fine lucencies against a neoplasm represent cystic sites filled with air (pseudocavitations).