Lung Field Segmentation Research Articles

Non-local attention and multi-task learning based lung segmentation in chest X-ray

Precise segmentation of lung field is a crucial step in chest radiographic computer-aided diagnosis system. With the development of deep learning, fully convolutional network based models for lung field segmentation have achieved great effect but are poor at accurate identification of the boundary and preserving lung field consistency. To solve this problem, this paper proposed a lung segmentation algorithm based on non-local attention and multi-task learning. Firstly, an encoder-decoder convolutional network based on residual connection was used to extract multi-scale context and predict the boundary of lung. Secondly, a non-local attention mechanism to capture the long-range dependencies between pixels in the boundary regions and global context was proposed to enrich feature of inconsistent region. Thirdly, a multi-task learning to predict lung field based on the enriched feature was conducted. Finally, experiments to evaluate this algorithm were performed on JSRT and Montgomery dataset. The maximum improvement of Dice coefficient and accuracy were 1.99% and 2.27%, respectively, comparing with other representative algorithms. Results show that by enhancing the attention of boundary, this algorithm can improve the accuracy and reduce false segmentation.

MWG-UNet: Hybrid Deep Learning Framework for Lung Fields and Heart Segmentation in Chest X-ray Images.

Deep learning technology has achieved breakthrough research results in the fields of medical computer vision and image processing. Generative adversarial networks (GANs) have demonstrated a capacity for image generation and expression ability. This paper proposes a new method called MWG-UNet (multiple tasking Wasserstein generative adversarial network U-shape network) as a lung field and heart segmentation model, which takes advantages of the attention mechanism to enhance the segmentation accuracy of the generator so as to improve the performance. In particular, the Dice similarity, precision, and F1 score of the proposed method outperform other models, reaching 95.28%, 96.41%, and 95.90%, respectively, and the specificity surpasses the sub-optimal models by 0.28%, 0.90%, 0.24%, and 0.90%. However, the value of the IoU is inferior to the optimal model by 0.69%. The results show the proposed method has considerable ability in lung field segmentation. Our multi-organ segmentation results for the heart achieve Dice similarity and IoU values of 71.16% and 74.56%. The segmentation results on lung fields achieve Dice similarity and IoU values of 85.18% and 81.36%.

Diagnostics of Multi Drug Resistant Tuberculosis in Chest Radiographs using Local Textures & Extreme Gradient Boosting

Abstract This study attempts to detect and differentiate Multi Drug Resistant (MDR) - Tuberculosis (TB) and Drug Sensitive (DS)-TB Chest Radiographs (CXR) using local texture descriptors and Ensemble Learning method. Studies report that CXR images contain likelihood information of the drug resistance which can be utilized computationally. Initially, CXR images are subjected to lung fields segmentation using Reaction Diffusion Level Set method. Further, Local Directional Texture Pattern (LDTP) features are extracted from the segmented lungs to characterize the localized textural variations. Extreme Gradient Boosting (XGBoost) classifier is employed to differentiate DS-TB and MDR-TB images. The obtained results demonstrate the ability of extracted LDTP features to characterize nonspecific textural inhomogeneities in images by operating on its principal directions. XGBoost algorithm provides maximum accuracy of 93% and true positive rate of 94.6% in detecting MDR-TB. As the proposed study differentiates the MDR-TB condition using CXR images, its computerized diagnostics could be used in the early screening and followup of TB ridden patients for public health infection control in any setting.

Development of Debiasing Technique for Lung Nodule Chest X-ray Datasets to Generalize Deep Learning Models.

Screening programs for early lung cancer diagnosis are uncommon, primarily due to the challenge of reaching at-risk patients located in rural areas far from medical facilities. To overcome this obstacle, a comprehensive approach is needed that combines mobility, low cost, speed, accuracy, and privacy. One potential solution lies in combining the chest X-ray imaging mode with federated deep learning, ensuring that no single data source can bias the model adversely. This study presents a pre-processing pipeline designed to debias chest X-ray images, thereby enhancing internal classification and external generalization. The pipeline employs a pruning mechanism to train a deep learning model for nodule detection, utilizing the most informative images from a publicly available lung nodule X-ray dataset. Histogram equalization is used to remove systematic differences in image brightness and contrast. Model training is then performed using combinations of lung field segmentation, close cropping, and rib/bone suppression. The resulting deep learning models, generated through this pre-processing pipeline, demonstrate successful generalization on an independent lung nodule dataset. By eliminating confounding variables in chest X-ray images and suppressing signal noise from the bone structures, the proposed deep learning lung nodule detection algorithm achieves an external generalization accuracy of 89%. This approach paves the way for the development of a low-cost and accessible deep learning-based clinical system for lung cancer screening.

Development of lung segmentation method in x-ray images of children based on TransResUNet.

Chest x-ray (CXR) is widely applied for the detection and diagnosis of children's lung diseases. Lung field segmentation in digital CXR images is a key section of many computer-aided diagnosis systems. In this study, we propose a method based on deep learning to improve the lung segmentation quality and accuracy of children's multi-center CXR images. The novelty of the proposed method is the combination of merits of TransUNet and ResUNet. The former can provide a self-attention module improving the feature learning ability of the model, while the latter can avoid the problem of network degradation. Applied on the test set containing multi-center data, our model achieved a Dice score of 0.9822. This novel lung segmentation method proposed in this work based on TransResUNet is better than other existing medical image segmentation networks.

Quantitative Evaluation of Changes in Three-Dimensional CT Density Distributions in Pulmonary Alveolar Proteinosis after GM-CSF Inhalation

Background: A previous clinical trial for autoimmune pulmonary alveolar proteinosis (APAP) demonstrated that granulocyte-macrophage colony-stimulating factor (GM-CSF) inhalation reduced the mean density of the lung field on computed tomography (CT) across 18 axial slice planes at a two-dimensional level. In contrast, in this study, we challenged three-dimensional analysis for changes in CT density distribution using the same datasets. Methods: As a sub-study of the trial, CT data of 31 and 27 patients who received GM-CSF and placebo, respectively, were analyzed. To overcome the difference between various shooting conditions, a newly developed automatic lung field segmentation algorithm was applied to CT data to extract the whole lung volume, and the accuracy of the segmentation was evaluated by five pulmonary physicians independently. For normalization, the percent pixel (PP) in a certain density range was calculated as a percentage of the total number of pixels from −1,000 to 0 HU. Results: The automatically segmented images revealed that the lung field was accurately extracted except for 7 patients with minor deletion or addition. Using the change in PP from baseline to week 25 (ΔPP) as the vertical axis, we created a histogram with 143 HU bins set for each patient. The most significant difference in ΔPP between GM-CSF and placebo groups was observed in two ranges: from −1,000 to −857 and −143 to 0 HU. Conclusion: Whole lung extraction followed by density histogram analysis of ΔPP may be an appropriate evaluation method for assessing CT improvement in APAP.

Lung Field Segmentation in Chest X-ray Images Using Superpixel Resizing and Encoder-Decoder Segmentation Networks.

Lung segmentation of chest X-ray (CXR) images is a fundamental step in many diagnostic applications. Most lung field segmentation methods reduce the image size to speed up the subsequent processing time. Then, the low-resolution result is upsampled to the original high-resolution image. Nevertheless, the image boundaries become blurred after the downsampling and upsampling steps. It is necessary to alleviate blurred boundaries during downsampling and upsampling. In this paper, we incorporate the lung field segmentation with the superpixel resizing framework to achieve the goal. The superpixel resizing framework upsamples the segmentation results based on the superpixel boundary information obtained from the downsampling process. Using this method, not only can the computation time of high-resolution medical image segmentation be reduced, but also the quality of the segmentation results can be preserved. We evaluate the proposed method on JSRT, LIDC-IDRI, and ANH datasets. The experimental results show that the proposed superpixel resizing framework outperforms other traditional image resizing methods. Furthermore, combining the segmentation network and the superpixel resizing framework, the proposed method achieves better results with an average time score of 4.6 s on CPU and 0.02 s on GPU.

A Research Paper on Lung Field Segmentation Techniques using Digital Image Processing

In this review study, we investigate various strategies for lung field segmentation by utilizing digital image processing. In chest radiographs (CXRs), lung field dissection segmentation is and will continue to be a crucial phase in the process of automatically evaluating pictures of this kind. We describe a method for the segmentation of the lung field that is based on a boundary map of high quality that was detected using a structured edge detector, which is a contemporary border detector (SED). A SED has previously been trained to recognize lung limits by using manually delineated lung sections in CXRs as training data. Following this step, the masked and tagged boundary map is converted into the active contour map (ACM). In conclusion, the lung contours that are created by following filter phases that are based on Gaussian and dilate features are the contours that have the highest rate of trust in the ACM. Our method is evaluated using aberrant lung pictures obtained from chest x-rays, and it is demonstrated to be superior, in terms of the amount of processing time required, to segmentation utilizing a universal contour map.

Explainable multi-instance and multi-task learning for COVID-19 diagnosis and lesion segmentation in CT images

Coronavirus Disease 2019 (COVID-19) still presents a pandemic trend globally. Detecting infected individuals and analyzing their status can provide patients with proper healthcare while protecting the normal population. Chest CT (computed tomography) is an effective tool for screening of COVID-19. It displays detailed pathology-related information. To achieve automated COVID-19 diagnosis and lung CT image segmentation, convolutional neural networks (CNNs) have become mainstream methods. However, most of the previous works consider automated diagnosis and image segmentation as two independent tasks, in which some focus on lung fields segmentation and the others focus on single-lesion segmentation. Moreover, lack of clinical explainability is a common problem for CNN-based methods. In such context, we develop a multi-task learning framework in which the diagnosis of COVID-19 and multi-lesion recognition (segmentation of CT images) are achieved simultaneously. The core of the proposed framework is an explainable multi-instance multi-task network. The network learns task-related features adaptively with learnable weights, and gives explicable diagnosis results by suggesting local CT images with lesions as additional evidence. Then, severity assessment of COVID-19 and lesion quantification are performed to analyze patient status. Extensive experimental results on real-world datasets show that the proposed framework outperforms all the compared approaches for COVID-19 diagnosis and multi-lesion segmentation.

Early severity prediction of BPD for premature infants from chest X-ray images using deep learning: A study at the 28th day of oxygen inhalation

Background and objectiveBronchopulmonary dysplasia is a common respiratory disease in premature infants. The severity is diagnosed at the 56th day after birth or discharge by analyzing the clinical indicators, which may cause the delay of the best treatment opportunity. Thus, we proposed a deep learning-based method using chest X-ray images of the 28th day of oxygen inhalation for the early severity prediction of bronchopulmonary dysplasia in clinic. MethodsWe first adopted a two-step lung field extraction method by combining digital image processing and human-computer interaction to form the one-to-one corresponding image and label. The designed XSEG-Net model was then trained for segmenting the chest X-ray images, with the results being used for the analysis of heart development and clinical severity. Therein, Six-Point cardiothoracic ratio measurement algorithm based on corner detection was designed for the analysis of heart development; and the transfer learning of deep convolutional neural network models were used for the early prediction of clinical severities. ResultsThe dice and cross-entropy loss value of the training of XSEG-Net network reached 0.9794 and 0.0146. The dice, volumetric overlap error, relative volume difference, precision, and recall were used to evaluate the trained model in testing set with the result being 98.43 ± 0.39%, 0.49 ± 0.35%, 0.49 ± 0.35%, 98.67 ± 0.40%, and 98.20 ± 0.47%, respectively. The errors between the Six-Point cardiothoracic ratio measurement method and the gold standard were 0.0122 ± 0.0084. The deep convolutional neural network model based on VGGNet had the promising prediction performance, with the accuracy, precision, sensitivity, specificity, and F1 score reaching 95.58 ± 0.48%, 95.61 ± 0.55%, 95.67 ± 0.44%, 96.98 ± 0.42%, and 95.61±0.48%, respectively. ConclusionsThese experimental results of the proposed methods in lung field segmentation, cardiothoracic ratio measurement and clinic severity prediction were better than previous methods, which proved that this method had great potential for clinical application.

Lung Field Segmentation Algorithm Based on Improved U-Net

Lung Field Segmentation Algorithm Based on Improved U-Net

Special Convolutional Neural Network for Identification and Positioning of Interstitial Lung Disease Patterns in Computed Tomography Images

In this paper, automated detection of interstitial lung disease patterns in high resolution computed tomography images is achieved by developing a faster region-based convolutional network based detector with GoogLeNet as a backbone. GoogLeNet is simplified by removing few inception models and used as the backbone of the detector network. The proposed framework is developed to detect several interstitial lung disease patterns without doing lung field segmentation. The proposed method is able to detect the five most prevalent interstitial lung disease patterns: fibrosis, emphysema, consolidation, micronodules and ground-glass opacity, as well as normal. Five-fold cross-validation has been used to avoid bias and reduce over-fitting. The proposed framework performance is measured in terms of F-score on the publicly available MedGIFT database. It outperforms state-of-the-art techniques. The detection is performed at slice level and could be used for screening and differential diagnosis of interstitial lung disease patterns using high resolution computed tomography images.

Automatic creation of annotations for chest radiographs based on the positional information extracted from radiographic image reports

Background and objectiveIn this study, we tried to create a machine-learning method that detects disease lesions from chest X-ray (CXR) images using a data set annotated with extracted CXR reports information. We set the nodule as the target disease lesion. Manually annotating nodules is costly in terms of time. Therefore, we used the report information to automatically produce training data for the object detection task. MethodsFirst, we use semantic segmentation model PSP-Net to recognize lung fields described in the CXR reports. Next, a classification model ResNeSt-50 is used to discriminate the nodule in segmented right and left field. It also can provide attention map by Grad-Cam. If the attention region corresponds to the location of the nodule in the CXR reports, an attention bounding box is generated. Finally, object detection model Faster-RCNN was performed using generated attention bounding box. The bounding boxes predicted by Faster-RCNN were filtered to satisfy the location extracted from CXR reports. ResultsFor lung field segmentation, a mean intersection of union of 0.889 was achieved in our best model. 15,156 chest radiographs are used for classification. The area under the receiver operating characteristics curve was 0.843 and 0.852 for the left and right lung, respectively. The detection precision of the generated attention bounding box was 0.341 to 0.531 depending on the binary setting for attention map. Through object detection process, the detection precisions of the bounding boxes were improved to 0.567 to 0.800. ConclusionWe successfully generated bounding boxes with nodule on CXR images based on the positional information of the diseases extracted from the CXR reports. Our method has the potential to provide bounding boxes for various lung lesions which can reduce the annotation burden for specialists. Short abstractMachine learning for computer aided image diagnosis requires annotation of images, but manual annotation is time-consuming for medical doctor. In this study, we tried to create a machine-learning method that creates bounding boxes with disease lesions on chest X-ray (CXR) images using the positional information extracted from CXR reports. We set the nodule as the target lesion. First, we use PSP-Net to segment the lung field according to the CXR reports. Next, a classification model ResNeSt-50 was used to discriminate the nodule in segmented lung field. We also created an attention map using the Grad-Cam algorithm. If the area of attention matched the area annotated by the CXR report, the coordinate of the bounding box was considered as a possible nodule area. Finally, we used the attention information obtained from the nodule classification model and let the object detection model trained by all of the generated bounding boxes. Through object detection model, the precision of the bounding boxes to detect nodule is improved.

Performance of SURF and SIFT Keypoints for the Automated Differentiation of Abnormality in Chest Radiographs.

In this work, automated abnormality detection using keypoint information from Speeded-Up Robust feature (SURF) and Scale Invariant Feature Transform (SIFT) descriptors in chest Radiographic (CR) images is investigated and compared. Computerized image analysis using artificial intelligence is crucial to detect subtle and non-specific alterations of Tuberculosis (TB). For this, the healthy and TB CRs are subjected to lung field segmentation. SURF and SIFT keypoints are extracted from the segmented lung images. Statistical features from keypoints, its scale and orientation are computed. Discrimination of TB from healthy is performed using SVM. Results show that the SURF and SIFT methods are able to extract local keypoint information in CRs. Linear SVM is found to perform better with precision of 88.9% and AUC of 91% in TB detection for combined features. Hence, the application of keypoint techniques is found to have clinical relevance in the automated screening of non-specific TB abnormalities using CRs.

SHAPE CHARACTERIZATION OF MEDIASTINUM IN TUBERCULOSIS CHEST RADIOGRAPHS USING LEVEL SET SEGMENTATION

Mediastinum is considered as one of the substantial anatomical regions for the gross diagnosis of several chest related pathologies. The geometric variations of the mediastinum in Chest Radiographs (CXRs) could be utilised as potential image markers in the early detection of Tuberculosis (TB). This study attempts to segment mediastinum in CXRs using level sets for the shape characterization of TB conditions. The CXR images for this study are considered from a public database. An edge-based distance regularized level set evolution is employed to segment the lungs followed by a region-based Chan-Vese model that extracts mediastinum region. Features such as mediastinum area and lungs area are extracted from the segmented images. Further, mediastinum to lungs area ratio is calculated. Statistical analysis is performed on the features to differentiate normal and TB images. Results show that the proposed segmentation approach is able to segment the lungs and extract the mediastinum in CXRs. It is found that features namely mediastinum area and mediastinum to lungs area ratio are statistically significant in the differentiation of TB. Larger mediastinum area is observed in TB images as compared to normal. The performance of lung field segmentation is also observed to be in line with the literature. The mediastinum segmentation approach in CXRs obtains to be a novel method as compared to the existing methods. As the proposed approach based on mediastinum image analysis provides better shape characterization, the study could be clinically useful in the differentiation of TB conditions.

2D Statistical Lung Shape Analysis Using Chest Radiographs: Modelling and Segmentation.

Accurate information of the lung shape analysis and its anatomical variations is very noticeable in medical imaging. The normal variations of the lung shape can be interpreted as a normal lung. In contrast, abnormal variations of the lung shape can be a result of one of the pulmonary diseases. The goal of this study is twofold: (1) represent two lung shape models which are different at the reference points in registration process considering to show their impact on estimating the inter-patient 2D lung shape variations and (2) using the obtained models in lung field segmentation by utilizing active shape model (ASM) technique. The represented models which showed the inter-patient 2D lung shape variations in two different forms are fully compared and evaluated. The results show that the models along with standard principal component analysis (PCA) can be able to explain more than 95% of total variations in all cases using only first 7 principal component (PC) modes for both lungs. Both models are used in ASM-based segmentation technique for lung field segmentation. The segmentation results are evaluated using leave-one-out cross validation technique. According to the experimental results, the proposed method has average dice similarity coefficient of 97.1% and 96.1% for the right and the left lung, respectively. The results show that the proposed segmentation method is more stable and accurate than other model-based techniques to inter-patient lung field segmentation.

View Identification Assisted Fully Convolutional Network for Lung Field Segmentation of Frontal and Lateral Chest Radiographs

Locating lung field is a critical and fundamental processing stage in the automated analysis of chest radiographs (CXRs) for pulmonary disorders. During the routine examination of CXRs, using both frontal and lateral CXRs can benefit clinical diagnosis of cardiothoracic and lung diseases. However, the accurate segmentation of lung fields on both frontal and lateral CXRs is still challenging due to the blurry boundary of the lung field on lateral CXRs and the poor generalization ability of the models. Existing deep learning-based methods focused on lung field segmentation on frontal CXRs, and the generalization ability of these methods on the different type of CXRs (e.g., pediatric CXRs) and new lung diseases (e.g., COVID-19) has not been tested. In this paper, a view identification assisted fully convolutional network (VI-FCN) is proposed for the segmentation of lung fields on frontal and lateral CXRs simultaneously. The VI-FCN consists of an FCN branch for lung field segmentation and a view identification branch for identification of the frontal and lateral CXRs and for enhancing the lung field segmentation. To improve the generalization ability of VI-FCN, six public datasets and our frontal and lateral CXRs (over 2000 CXRs) were collected for training. The segmentation of lung fields on the Japanese Society of Radiological Technology (JSRT) dataset yields mean dice similarity coefficient (DSC) of 0.979 ± 0.008, mean Jaccard index ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\Omega $ </tex-math></inline-formula> ) of 0.959 ± 0.016, and mean boundary distance (MBD) of 1.023 ± 0.487 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mm</i> . Besides, the VI-FCN achieves mean DSC of 0.973 ± 0.010, mean <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\Omega $ </tex-math></inline-formula> of 0.947 ± 0.018, and mean MBD of 1.923 ± 0.755 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mm</i> for the segmentation of lung fields on our lateral CXRs. The experiments demonstrate the superior performance of the proposed VI-FCN over most of existing state-of-the-art methods. Moreover, the proposed VI-FCN achieves promising results on untrained pediatric CXRs and COVID-19 datasets.

Robust segmentation of lung in chest x-ray: applications in analysis of acute respiratory distress syndrome

BackgroundThis study outlines an image processing algorithm for accurate and consistent lung segmentation in chest radiographs of critically ill adults and children typically obscured by medical equipment. In particular, this work focuses on applications in analysis of acute respiratory distress syndrome – a critical illness with a mortality rate of 40% that affects 200,000 patients in the United States and 3 million globally each year.MethodsChest radiographs were obtained from critically ill adults (n = 100), adults diagnosed with acute respiratory distress syndrome (ARDS) (n = 25), and children (n = 100) hospitalized at Michigan Medicine. Physicians annotated the lung field of each radiograph to establish the ground truth. A Total Variation-based Active Contour (TVAC) lung segmentation algorithm was developed and compared to multiple state-of-the-art methods including a deep learning model (U-Net), a random walker algorithm, and an active spline model, using the Sørensen–Dice coefficient to measure segmentation accuracy.ResultsThe TVAC algorithm accurately segmented lung fields in all patients in the study. For the adult cohort, an averaged Dice coefficient of 0.86 ±0.04 (min: 0.76) was reported for TVAC, 0.89 ±0.12 (min: 0.01) for U-Net, 0.74 ±0.19 (min: 0.15) for the random walker algorithm, and 0.64 ±0.17 (min: 0.20) for the active spline model. For the pediatric cohort, a Dice coefficient of 0.85 ±0.04 (min: 0.75) was reported for TVAC, 0.87 ±0.09 (min: 0.56) for U-Net, 0.67 ±0.18 (min: 0.18) for the random walker algorithm, and 0.61 ±0.18 (min: 0.18) for the active spline model.ConclusionThe proposed algorithm demonstrates the most consistent performance of all segmentation methods tested. These results suggest that TVAC can accurately identify lung fields in chest radiographs in critically ill adults and children.

From 3D to 2D: Transferring knowledge for rib segmentation in chest X-rays

Chest X-rays are the most common type of biomedical radiologic exam, being widely adopted for the diagnosis of a myriad of illnesses in the thoracic region. Computed Tomography – even though being more expensive and rare – is also a useful tool for the detection of several illnesses and surgery planning, providing volumetric information. This paper proposes a methodology aiming to leverage the larger amounts of spatial information and lack of occlusion in tomographic images to aid in the rib segmentation of 2D X-ray images by means of Domain Adaptation. We perform extensive quantitative and qualitative experiments to test the capabilities of this methodology in segmenting ribs in 7 X-ray datasets with distinct visual features, using 6 different metrics and without any use of rib segmentation labels from the target image sets. In order to encourage reproducibility, all data and code used in this research is publicly available online, including a new 2D Digitally Reconstructed Radiograph generated from tomographic data and a new pixel-level label map for the JSRT Chest X-ray dataset. We also publicize our generalizable pretrained models for both rib segmentation in Chest X-rays and lung field segmentation in Digitally Reconstructed Radiographs. Results show that the proposed pipeline outperforms shallow rib segmentation baselines in almost all quantitative metrics and produce higher fidelity pixel-map predictions than simply using the pretrained Neural Networks on the flattened 3D data, mainly in datasets where domain shift is more pronounced. The use of Conditional Domain Adaptation also allows the method to perform inference on all 7 X-ray datasets using one single model, achieving over 0.856 of AUC on OpenIST and 0.934 of AUC on JSRT, with Dice scores of 0.68 and 0.69 in these two datasets, respectively.

Lung Field Segmentation in Chest X-rays: A Deformation-Tolerant Procedure Based on the Approximation of Rib Cage Seed Points

The delineation of bone structures is a crucial step in Chest X-ray image analysis. In the case of lung field segmentation, the main approach after the localization of bone structures is either their individual analysis or their suppression. We prove that a very fast and approximate identification of bone points that are most probably located inside the lung area can help in the segmentation of the lung fields, without the need for bone structure suppression. We introduce a deformation-tolerant region growing procedure. In a two-step approach, a sparse representation of the rib cage is guided to several support points on the lung border. We studied and dealt with the presence of other bone structures that interfere with the lung field. Our method demonstrated very robust behavior even with highly deformed lung appearances, and it achieved state-of-the-art performance in segmentations for the vast majority of evaluated CXR images. Our region growing approach based on the automatically detected rib cage points achieved an average Dice similarity score of 0.92 on the Montgomery County Chest X-ray dataset. We are confident that bone seed points can robustly mark a high-quality lung area while remaining unaffected by different lung shapes and abnormal structures.