Artificial Intelligence for chest imaging against COVID-19: an insight into image segmentation methods

The coronavirus disease 2019 (COVID-19), caused by the Severe Acute Respiratory Syndrome Coronavirus 2, emerged in late 2019 and soon developed as a pandemic leading to a world health crisis. Chest imaging examination plays a vital role in the clinical management and prognostic evaluation of COVID-19 since the imaging pathological findings reflect the inflammatory process of the lungs. Particularly, thanks to its highest sensitivity and resolution, Computer Tomography chest imaging serves well in the distinction of the different parenchymal patterns and manifestations of COVID-19. It is worth noting that detecting and quantifying such manifestations is a key step in evaluating disease impact and tracking its progression or regression over time. Nevertheless, the visual inspection or, even worse, the manual delimitation of such manifestations may be greatly time-consuming and overwhelming for radiologists, especially when pressed by the urgent needs of patient care. Image segmentation tools, powered by Artificial Intelligence, may sensibly reduce radiologists' workload as they may automate or, at least, facilitate the delineation of the pathological lesions and the other regions of interest for disease assessment. This delineation lays the basis for further diagnostic and prognostic analyses based on quantitative information extracted from the segmented lesions. This chapter overviews the Artificial Intelligence methods for the segmentation of chest Computed Tomography images. The focus is in particular on Deep Learning approaches, as these have lately become the mainstream approach to image segmentation. A novel method, leveraging attention-based learning, is presented and evaluated. Finally, a discussion of the potential, limitations, and still open challenges of the field concludes the chapter.