Commun. PMC legacy view Automatic lung segmentation from High Resolution Com-puted Tomography (HRCT) images is an important part of the Computer Aided Diagnosis (CAD) systems for the lung. Moreover, it can create compressed features that use global information more effectively because it uses the mean value of each channel of the input for compression. We used X-ray images collected from three public datasets (the Montgomery dataset [22], the Japanese Society of Radiological Technology (JSRT) dataset [23], and the Shenzhen dataset [24]). Traditional lung segmentation methods do not rely on the dataset labeled by professional radiologists, so they are easy to implement. The accuracy and reliability of lung segmentation algorithms on demanding cases primarily relies on the diversity of the training data, highlighting the importance of data diversity compared to model choice. and L.Y. Med Phys. An attention module serves to highlight the values of important features, which are elusive during judgment, by emphasizing important features and removing features unnecessary for learning. After the preprocessing denoising with Wiener filter, we fuse texture features based on GLCM and deep features based on U-Net for the segmentation contour. Yu Q., Xie L., Wang Y., Zhou Y., Fishman E.K., Yuille A.L. Birkbeck N, Kohlberger T, Zhang J, Sofka M, Kaftan J, Comaniciu D, Zhou SK. Methods 990994. Methods Programs Biomed. 319. PMC Bethesda, MD 20894, Web Policies Plva, B., Jmda, B., Jna, B. Automatic lung segmentation in routine imaging is primarily a data. 2426 April 2018. http://creativecommons.org/licenses/by/4.0/, https://lhncbc.nlm.nih.gov/publication/pub9931, http://archive.nlm.nih.gov/repos/chestImages.php. Our model improves about 5% dice coefficient and 9% Jaccard Index for the private lung segmentation datasets compared with the traditional U-Net model. Finally, we apply a 11 convolution layer and then use the "Sigmoid" activation function to output the mask. 814 September 2018; pp. For fragment (FP), we use the connected domain filtering algorithm. & Informatics, R. X. J. I. T. o. I. Jaeger, S. et al. Figure 3 shows an example of computation for GLCM and NGLCM where every cell contains the probability value. This paper presents a fully automatic method for identifying the lungs in three-dimensional (3-D) pulmonary X-ray CT images. 36 September 2018; pp. Bethesda, MD 20894, Web Policies https://doi.org/10.1109/TII.2021.3059023 (2021). 5shows the specific functions of these two algorithms. Method 4: U-net architecture + Efficientnet-b4 encoder + LeakyReLU. OnLine 17, 113. https://doi.org/10.1186/s12938-018-0544-y (2018). The number of neurons of the hidden layer is Cr11, where r is a hyper-parameter to control a learnable parameters overhead. Would you like email updates of new search results? In this study, we propose a deep learning-based method to segment lung areas in chest X-rays. The blue bar indicates DSC; the orange bar indicates SEN. We further show about the training performance (according to equation (5)) for U-Net, GU, and ours in Figure 7. Diniz, J. et al. Health Inform. A fully automated and three-dimensional segmentation method for the identification of the pulmonary parenchyma in thorax X-ray computed tomography (CT) datasets is proposed, which proved to be fit for the use in the framework of a CAD system for malignant lung nodule detection. The X-attention module focuses on the important features required for segmentation of lung areas by combining the features extracted through channel attention and spatial attention for the input feature map FinputRCHW. The channel attention highlights the areas of the input feature maps corresponding to what in the learning process by readjusting the features between the channels of the input feature map. In the Recurrent Saliency Transformation Network [20], the segmentation probability map from the previous iterations is repeatedly converted into spatial weights that are, in turn, applied in the current iteration. 1, Pts 13, p. 453. prepared the dataset and confirmed abnormalities. Biomed. We conjectured that this result was attributed to high variability of lung segmentation masks due to the different lung shapes and borders in the Shenzhen dataset compared to the other two datasets [25]. Figure1 and Formulas (13) show how this is achieved. Conceptualization, B.-D.L. Bildverarbeitung fr die Medizin 2020. https://doi.org/10.1007/978-3-658-29267-6_17 (2020). False Negative (FN): the model prediction is a counterexample, but it is a positive example. The average accuracy on JSRT, MC, and local data sets are 97.1%, 97.7%, and 94.2%, respectively. The input feature map (Finput) can generate feature maps of various scales by passing through 3 3 convolution layers consecutively. However, the clinical applicability of these approaches across diseases remains limited. With the Montgomery dataset and the JSRT dataset, the segmentation performance varies by up to 2.5% and 1.4%, respectively, depending on the locations of the X- and Y-attention modules, whereas the performance gain was marginal with the Shenzhen dataset. J.L. 2022 Sep 20;12(10):2274. doi: 10.3390/diagnostics12102274. The novel aspect of the proposed method is the self-attention module, where the outputs of the channel and spatial attention modules are combined to generate attention maps, with each highlighting those regions of feature maps that correspond to what and where to attend in the learning process, respectively. In addition, the automatic lung segmentation model is poor in dealing with severe lung deformation caused by congenital or acquired factors. Deep residual learning for image recognition. Based on the deep neural network, Lamin Saidy et al.introduce the knowledge of graphic morphology to solve the problem of fragments in lung segmentation24. Radiographics 35:10561076. In this case, it is adjusted to the same number of channels as that of Finput through MLP, which is used in the channel attention, to enable element-wise multiplication between attention maps. This method uses the pre-trained Efficientnet-b4 as the encoder and uses the residual block and LeakyReLU to optimize the decoder. Article Therefore, we propose the Y-attention module to effectively utilize global features in input images. LeCun Y., Bengio Y., Hinton G. Deep learning. In this study, we evaluated the efficacy of our model for lung segmentation on the JSRT, MC, and Haut datasets. Automatic tuberculosis screening using chest radiographs. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR); June 2015; Las Vegas, NV, USA. The Montgomery datasetpublished by the state health department of Montgomery, Alabama, in the U.S.comprises a total of 138 images: 80 images of patients with tuberculosis and 58 images of people without disease. The connected domain filtering algorithm and flood filling algorithm. To verify the validity of the attention module, experiments were conducted for various configurations of the attention module by combining it with U-Net. Lung segmentation in Computed Tomography (CT) images plays a vital role in the diagnosis, detection and three-dimensional visualization of lung nodules. Comparative performance of lung segmentation on chest X-ray images. Proceedings of the 2007 IEEE International Conference on Acoustics, Speech, and Signal Processing; June 2007; Honolulu, HI, USA. https://doi.org/10.1049/iet-ipr.2016.0526 (2017). J. Roentgenol. Therefore, our review focuses on attention-based approaches for segmentation. We use random gamma, blur, horizontal flip, normalization, and other data enhancement methods. Publishers Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. However, the image processing methods have relatively simple algorithms and exhibit poor segmentation performance when the input image contains noise. Radiation therapy (RT) is one of the primary treatment modalities for lung cancer. The Haut dataset contains some chest radiographs that are seriously blurred, obscured, and deformed. Since its introduction in SENet [16], channel attention has attracted significant research interest and proved its potential in improving the performance of deep neural networks. Web of Science, PubMed, and IEEE Xplore. The input images were not normalized but the brightness of the images was adjusted through histogram equalization. The second line is that foreign bodies (various medical devices) block the lung field, and the segmentation effect is relatively poor. NGLCM calculation: (b)-(b), (c)-(c), (d)-(d), (e)-(e). Simonyan K., Zisserman A. Bethesda, MD 20894, Web Policies investigating transferability in COVID-19 CT image segmentation. Work fast with our official CLI. Huang Z., Wang X., Huang L., Huang C., Wei Y., Liu W. Ccnet: Criss-cross attention for semantic segmentation; Proceedings of the IEEE International Conference on Computer Vision; Seoul, Korea. Automated segmentation of anatomical structures is a crucial step in image analysis. Singh, A. et al. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI); October 2015; Munich, Germany. When we train our DCNN, we are essentially trying to run an optimization algorithm (in our case,Nadam) to minimize the chosen loss function. official website and that any information you provide is encrypted An effective deep neural network for lung lesions segmentation from COVID-19 CT images. government site. 27 October2 November 2019; pp. The .gov means its official. Method 3: U-net architecture + Efficientnet-b4 encoder + Residual block. Assist. Image filtering In 3D computer graphics, a depth map is a two-dimensional gray-scale image. Gupta A., Martens O., Moullec Y. L., Saar T. Methods for increased sensitivity and scope in automatic segmentation and detection of lung nodules in CT images. A survey on deep learning in medical image analysis. the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Residual block is the most important module in Resnet28. The only difference is in the part where the input is less than 0. Currently, researchers are attempting to enhance lung segmentation performance not only by developing complex image segmentation network structures but also by using various techniques such as attention modules. High-resolution CT scan findings in patients with symptomatic scleroderma-related interstitial lung disease. The method consists of 6 main steps: (1) Seg- mentation of tracheabronchial tree. Long J., Shelhamer E., Darrell T. Fully convolutional networks for semantic segmentation. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate. Rajpurkar P., Irvin J., Zhu K., Yang B., Mehta H., Duan T., Ding D., Bagul A., Langlotz C., Shpanskaya K., et al. We improved the U-Net network by using the pre-training Efficientnet-b4 as the encoder and the Residual block and the LeakyReLU activation function in the decoder. Lung cancer is the leading cause of cancer-related mortality for males and females. Automatic lung segmentation in CT images with accurate handling of the hilar region. 1620 June 2019. Moreover, the training time of ours is shortest, showing that the complexity is lower and it is easy to perform our method. However, the segmentation of infected regions in CT slices still faces many challenges. A challenge of deep learning for medical image processing is that it often provides few samples, and U-Net still performs well under this limitation. Tables 1, 2, and 4 list the accuracy, specificity, sensitivity, Dice coefficient, and Jaccard index for different methods on JSRT, MC, and Haut. All authors have read and agreed to the published version of the manuscript. Segmentation of the lung becomes challenging due to several reasons: (1) non-pathological changes: the shape and size of the lung vary with age, gender, and heart size; (2) pathological changes: the opacity caused by severe lung disease reaches a high-intensity value5; (3) foreign body coverage, such as the lung field, is obscured by the patient's clothes or medical equipment (pacemaker, infusion line, medical catheter)6. The architecture of U-Net with EfficientNet-b4 Encoder. The accuracy of this kind of algorithm is far lower than that of neural network modeling6,9. Pers. 234241. The accuracy is comparable to that obtained in the advanced literature in recent years. In this paper, the downsampling is like an encoder including 3 times of operations with two 33 convolutional networks followed by a rectified linear unit (ReLU) and a 22 max pooling layer. and J.L. 22, 842851. Our goal was to provide an automatic, clinically applicable and reliable lung segmentation procedure. The element of the normalized gray-level co-occurrence matrix (NGLCM) is defined as follows: The values of parameters x, y at different directions. The network architecture used in this work has five coding layers and five decoding layers. This paper has proposed a completely automatic algorithm for recognition and segmentation of lungs in 3D pulmonary X-ray CT images. Automatic lung segmentation in routine imaging is primarily a data diversity problem, not a methodology problem. A common characteristic across existing studies on lung segmentation is the absence of learning data wherein the contour of the lung is hidden or the lung shape is deformed. But now, the shallow network can not significantly improve the network performance. National Library of Medicine The same patterns appear with the Y-attention module; application of the Y-attention module at shallow layers performed slightly better than those at deeper layers. ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases. 29993007. The U-Net which yields more accurate segmentation is based on the fully convolutional network [37] and suitable for few medical image training. Correspondence to Qaisar A. Then, we, respectively, show and discuss the performance of denoising, segmentation, and training process by comparing with the baseline methods. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. For each dataset, data were randomly split into three subsetstraining (70%), validation (10%), and testing (20%). The task of labelling separate regions within the lung poses more of a challenge. Sluimer I., Schilham A., Prokop M., van Ginneken B. CXRs are one of the most commonly prescribed medical imaging procedures with the voluminous CXR scans placing significant load on radiologists and medical practitioners. Most of the reported work on lung segmentation is based on mild lesions or healthy CXR images. Anyone you share the following link with will be able to read this content: Sorry, a shareable link is not currently available for this article. Spinal cord detection in planning CT for radiotherapy through adaptive template matching, IMSLIC and convolutional neural networks. In image segmentation tasks, especially medical image segmentation, U-Net8 is undoubtedly one of the most successful methods. [11] extended Structure Correcting Adversarial Network (SCAN) [12], which is the first attempt to use adversarial learning for lung segmentation on chest X-rays, by adopting Attention U-Net and Focal Tversky loss [13] for the generator network and its corresponding loss function, respectively. In such automatic disease identification systems, the performance of disease diagnosis is dependent on the image segmentation performance. Finput and Finput', which is an output of the deeper layer, are different feature maps of different scales. Lung Radiomics Features Selection for COPD Stage Classification Based on Auto-Metric Graph Neural Network. Schematic overview of the training and testing performed. All authors reviewed the manuscript. Background: Automated segmentation of anatomical structures is a crucial step in image analysis. Scientific Reports https://doi.org/10.1007/s11548-019-01917-1 (2019). In IEEE conference on computer vision and pattern recognition (2017). & Moa, B. J. E. S. w. A. Multi-stage transfer learning for lung segmentation using portable X-ray devices for patients with COVID-19. Hard examples with deformed lung shapes or ambiguous cardiac silhouettes contained in the Shenzhen dataset. Please enable it to take advantage of the complete set of features! U.S. Department of Energy Office of Scientific and Technical Information. [(accessed on 21 August 2020)]; Open Access Biomedical Image Search Engine. We collected public datasets and, Segmentation results for selected cases from routine data. and M.K. Sci. ; writingoriginal draft preparation, B.-D.L. https://doi.org/10.1109/TMI.2019.2893944 (2019). Academic Editor: Sahfqat Ullah Khan. Very deep convolutional networks for large-scale image recognition. Russell A. M., Maher T. M. Detecting anxiety and depression in patients diagnosed with an interstitial lung disease. New York, NY, Yang J, Veeraraghavan H, Armato SG 3rd et al (2018) Autosegmentation for thoracic radiation treatment planning: a grand challenge at AAPM 2017. 285298. Sema, Candemir, Sameer, Radiology, A. J. I. J. o. C. A. The automatic segmentation of the lung region for chest X-ray (CXR) can help doctors diagnose many lung diseases. Dai W., Doyle J., Liang X., Zhang H., Dong N., Li Y., Xing E.P. The authors declare no conflict of interest. The output of the MLP is passed through batch normalization (BN) and the rectified linear unit function (ReLU), which is an activation function. Hence, the combination of the deep features and texture features is a necessary step in lung segmentation. Pattern Anal. Suppose RelU is used as the activation function of the middle layer when the gradient of the backpropagation process is 0. Revised 23 Feb 2022. For instance, the Y-attention module requires only five 3 3 filters to be learned. Finally, the paper is concluded in Section 5. Radiomics: extracting more information from medical images using advanced feature analysis. Example of GLCM. 71327141. We randomly selected 2785 samples and invited doctors (Wenlian Wang and Junkui Deng from Nanyang Central Hospital) to label the image's lung fields. That's a contradictory problem, but the residual block effectively solves the contradiction of avoiding the "vanishing gradient" when deepening the network. The two-dimensional convolution layer continues to extract image information. Moreover, it showed comparable performance to XLSor, which is a state-of-the-art deep learning model for lung segmentation on chest X-ray images, on all of three public datasets, thus validating the method. https://doi.org/10.2214/ajr.174.1.1740071 (2000). These images are stored in PNG format with 20482048 pixels having 12 bits grayscale. However, manually segmenting the lungs is tedious and taking lots of time for the large-sized CT databases. Haralick R. M., Shanmugam K., Dinstein I. H. Textural features for image classification. Figure4 shows the performance of our lung segmentation model in two benchmark datasets. The NIH Chest X-ray Dataset comprises 112,120 X-ray images with disease labels from 30,805 unique patients. The study was approved by the Ethics Committee of the Henan University of Technology, all methods were carried out by relevant guidelines and regulations. Awais M., Ulas B., Brent F., et al. Ginneken, B. V., Stegmann, M. B. pp. Ferreira J. R., Koenigkam-Santos M., Cipriano F. E. G., Fabro A. T., de Azevedo-Marques P. M. Radiomics-based features for pattern recognition of lung cancer histopathology and metastases. Segmentation of differential structures on computed tomography images for diagnosis lung-related diseases. In general, the performance of our lung segmentation network is comparable to that of the excellent lung segmentation network proposed in the literature in recent years. We collected public datasets and two datasets from the routine. Consequently, existing methods achieve low segmentation performances for chest X-ray images containing hidden lung contours or deformed lung shapes. For lung segmentation in computed tomography, a variety of approaches exists, involving sophisticated pipelines trained and validated on different datasets. The fine features can be highlighted by applying the attention modules consecutively rather than by applying X(i) and Y(j)(i,j{1,2,3,4}) separately. Using different deep learning approaches, mean Dice similarity coefficients (DSCs) on test datasets varied not over 0.02. National Library of Medicine LeakyReLU29 was used as the activation function. Lin T.-Y., Goyal P., Girshick R., He K., Dollar P. Focal loss for dense object detection; Proceedings of the IEEE International Conference on Computer Vision; Venice, Italy. The value of DSC is between zero and one: where TP is true positive and FN is false negative. (g) Ground truth. Otsu N. A threshold selection method from gray-level histograms. Recurrent saliency transformation network: Incorporating multi-stage visual cues for small organ segmentation; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition; Salt Lake City, UT, USA. On the same hand, the ANODE database originating from NELSON contains 55 anonymized thoracic CT scans, provided by the University Medical Center Utrecht, to evaluate lung nodule detection algorithms. Ablations of the encoder and decoder sub-block approach on the JSRT, MC, and Haut are reported in Tables 1, 2, and 4. Google Scholar. (5) Lung separation if necessary. Signal Process. Then we present the DSC (average value) and SEN of segmentation results on testing dataset with the T on training dataset using our method and four compared methods in Table 2. In the meantime, to ensure continued support, we are displaying the site without styles

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