# Lung Field Segmentation in Chest X-rays:
## 1 Introduction  
The Chest X-ray (CXR) is a common and widely used imaging modality in everyday clinical practice, aiming to visualize disorders of the bony thorax and the organs of the thoracic cavity [1, 2]. Even though it is cheap, readily available and technically simple, it can reveal a significant amount of information regarding the patient's health, thus providing important clues for an accurate diagnosis [1]. Over the years, CXR has inevitably faced the rapid progress of medical imaging technology and has been subjected to criticism regarding its poor diagnostic sensitivity, which has to be counterbalanced by an accurate, detailed and time-consuming interpretation by a radiologist [1]. Radiograph interpretation is prone to error and discrepancies, and even an experienced radiologist using a systematic approach can miss 10-15% of pathologic lesions [1]. A discrepancy is defined as the extent of disagreement between the opinions of two radiologists regarding a diagnosis [3]. Studies report a day-to-day error rate of 3-5% per observer and a disagreement rate of 5-9% between two observers [3]. In a studyof 100 chest radiographs, concordance between three expert radiologists was 61%, pointing out the significant interobserver variation in CXR interpretation [4]. The structures and pathologies depicted on a CXR often have similar opacities or overlap one another, making the discrimination of fine lesions difficult. The abovementioned weaknesses paved the way for Computer-Aided Diagnosis (CAD) in chest radiography, aiming to improve the diagnostic accuracy. The first attempt to implement CAD in CXR interpretation dates back to 1963 when [PERSON] et al. proposed a method to determine the probability of a lung nodule being cancerous, based on the systematic classification of unique descriptive features of lung lesions [5]. Since then, CAD in chest radiography has focused on automating discreet steps of CXR interpretation such as size and shape measurements, contour delineation and nodule detection [6]. Moreover, CAD research has focused on developing algorithms suited for one disease at a time (pneumonia, lung cancer lesions, tuberculosis, pneumothorax) and not an all-in-one automated solution [6]. Over the past few years, there has been an increasing number of published works aiming to identify various lung pathologies by using deep learning approaches such as Convolutional Neural Networks (CNN) [7; 8; 9; 10]. Automated CXR interpretation could drastically alleviate the increasing workload of radiologists by complementing their diagnostic procedures and acting as a \"second opinion\" [11]. From a technical standpoint, a typical CXR analysis algorithm comprises the following discrete steps: defining the Region Of Interest (ROI), identifying specific imaging features of the ROI and using a machine learning strategy to classify the detected pathology [12]. In the case of CXR analysis, the ROI is the lung region within the bony thorax, and its correct identification is a crucial prerequisite. Accurately delineating the lung boundaries is a very challenging image analysis problem, and it is well studied in the literature. The majority of available algorithms are evaluated based on anatomically \"correct\" CXRs and are sometimes prone to error due to anatomical variations, chest deformities and incorrect positioning of the patient [12]. In the era of the ongoing pandemic of the novel coronavirus disease 2019 (COVID-19), which has pushed the critical capacity and efficiency of healthcare facilities to their limits, fast and accurate diagnosis of positive cases has become a matter of crucial importance. [PERSON] et al. proposed a weakly supervised deep-learning method for the detection of COVID-19 from chest Computed Tomography (CT) scans [13]. The aim of their approach was to provide a solution for the fast detection of COVID-19, minimizing the time-consuming need for manual labeling of cases by radiologists.