# Lung Field Segmentation in Chest X-rays:
## 1 Introduction
Their multiscale learning scheme was able to cope with variations in the size and location of the lesions, since pathological lesions are usually small for mild cases. They evaluated their algorithm on a chest CT dataset from multiple centers and CT scanners. Discrepancies in image appearance due to the use of different classes of scanners or variable image acquisition techniques and issues regarding the low visibility of anatomical structures are addressed by the studies of [PERSON] et al. and [PERSON] et al., both focusing on cardiac imaging [14; 15]. The former authors applied a joint segmentation method based on a multiview two-task (MVTT) recursive attention model to segment the left atrium of the heart and its scars by mimicking the inspection sequence of radiologists, whereas the latter authors used a deep learning method by developing a multiview recurrent aggregation network (MV-RAN) for the segmentation of each echocardiographic sequence during a full cardiac cycle. [PERSON] et al. also applied an attention model to segment the prostate gland, which exhibits various morphological characteristics [16]. For the segmentation of the lung area, we propose a region growing method where the enclosed rib cage structure is promoted as a peculiarity for a very robust initial demarcation. The specifics of our method along with a brief introduction to the dataset used and the evaluation results follow in later sections. At this point, we review related published works regarding the segmentation of lung regions and ribs, focusing on their strengths and weaknesses and comparing their approaches with ours. The presence of several insufficiently separated bone structures, some of them irrelevant to the lung area, remains a big concern in many works. [PERSON] et al. introduce a deep learning technique for bone shadow exclusion and lung segmentation in 2D CXRs, to assist radiologists in the diagnostic process [17]. However, the introduced additional preprocessing step, which is very complex most of the time and computationally very demanding, can contribute to the accumulation of errors when used within the context of the overall lung area segmentation. In contrast, in our work we prove that a very simplistic identification of a few rib cage points is enough for quite an efficient overall lung area segmentation algorithm without the intention to preprocess the initial CXR image. [PERSON] et al. attempted to segment the rib cage by using an iterated contextual single pixel classification approach [18]. The overall relabeling process guarantees that any errors appear in a structured way. They confirmed the challenges of rib cage segmentation, emphasizing the risk of confusion between ribs and clavicles and, besides the poor visibility of the ribs, the potential problem of differences in appearance and size for rib structures localized at the top of the lung fields compared with the rest of the image. [PERSON] et al. qualified ribs overlapping with the lung field as the main noise in lung node detection, pointed out the fact that their shapes are different from each other and subsequently used different templates to describe them [19]. The beforementioned authors introduced a sophisticated filtering process of real rib structures, also employing graph theory. [PERSON] et al. introduced a unique method addressing both rib segmentation and anatomical labeling in chest radiographs [20]. Based on the idea that the additional information of neighboring ribs leads to a more precise detection of a single rib, they proposed a sequential processing scheme, extending the Mask Region-based Convolutional Neural Network (R-CNN) architecture. As commonly done, [PERSON] et al. considered the segmentation of the two lung fields as a given [21]. In their approach, the generalized Hough transform is employed to localize the lower boundary of the ribs, and based on this, a novel bilateral dynamic programming algorithm delineates the upper and lower boundaries of each rib within the lung area.