Our model with a pre-trained Efficientnet-b4 network obtained an accuracy of 99.4% on the Haut dataset with 99.5% of specificity, 99.1% of sensitivity, 98.8% of dice coefficient, and 97.7% of Jaccard index with pre-trained Efficientnet-b4 base network, which is very encouraging and establishes the efficiency of our method. In reference [35], the formulas of 14 features (Angular Second Moment, Contrast, Correlation, Difference Variance, Difference Entropy, etc.) The networks that applied XLSor and the proposed attention module show relatively similar shapes to the lung area of the ground truth as compared to U-Net or Attention U-Net. Hwang, S. & Park, S. Accurate lung segmentation via network-wise training of convolutional networks. Bethesda, MD 20894, Web Policies Radiomics: extracting more information from medical images using advanced feature analysis. This finding is consistent with the results of the empirical investigations conducted using SENet. We used the data enhancement tool "Albumentations" (https://github.com/albumentations-team/albumentations). Ngo, T. A. Comparison of sensitivity according to the location of the attention modules. Lung segmentation is usually the first step in lung CT images . sharing sensitive information, make sure youre on a federal Souza, J. et al.designed an automatic lung segmentation and reconstruction method based on a depth neural network23. The architecture of U-Net with EfficientNet-b4 Encoder. 32, 8793. (a) Lung segmentation results of the U-Net + X(3)+X(4)+Y(3)+Y(4) structure; (b) lung segmentation results of the U-Net + Y(1)+Y(2)+Y(3)+Y(4) structure; (c) lung segmentation results of the U-Net + X(1)+X(2)+Y(1)+Y(2) structure. Liu, Y., Wang, X., Wang, L., Liu, D. J. Segmentation and Image Analysis of Abnormal Lungs at CT: Current Approaches, Challenges, and Future Trends. 10.1787/19991312, Mansoor A, Bagci U, Foster B et al (2015) Segmentation and image analysis of abnormal lungs at CT: current approaches, challenges, and future trends. Our model generally achieves excellent segmentation scores in dealing with two benchmark datasets (mild disease, no foreign body occlusion, high image quality). vol. Pham T. D. Estimating parameters of optimal average and adaptive wiener filters for image restoration with sequential Gaussian simulation. This paper describes the development of an algorithm to automatically generate seeds for lung CT (Computed Tomography) segmentation. Sci Rep 12, 8649 (2022). Google Scholar. Eur Radiol Exp 4, 50 (2020). An automatic method for lung segmentation and reconstruction in chest X-ray using deep neural networks. 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. Image segmentation is invaluable because it can distinguish specific interest areas by drawing boundary lines in an input image and identifying diseases by segmenting organs and tumors in medical images. Based on these advantages, we choose U-Net as the framework of the automatic lung segmentation model. Comput. 319. Please enable it to take advantage of the complete set of features! We collected public datasets and two datasets from the routine. Therefore, our review focuses on attention-based approaches for segmentation. U-Net generates much higher DSC value than GLCM, while the SEN value of U-Net is almost the same with GLCM. Pers. Figures2 and 3 show the architecture of our model and the detail of the decoder sub-block. CT image sequence restoration based on sparse and low-rank decomposition. U-Net was used in this experiment with ResNet101 as the backbone network. The value of the part where the input of ReLU is less than 0 is 0, while the value of the part where the input of LeakyReLU is less than 0 is negative and has a slight gradient. Original images (. We did not apply data augmentation to any of the deep learning models used in this study to observe the effects of the proposed attention modules. Learn more (f) Ours. J. Roentgenol. We collected public datasets and, Segmentation results for selected cases from routine data. They can also be made available in Dicomformat upon request. Hence, the combination of the deep features and texture features is a necessary step in lung segmentation. Segmentation of anatomical structures in chest radiographs using supervised methods: a comparative study on a public database. 2021 May;48(5):2468-2481. doi: 10.1002/mp.14782. ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases. https://doi.org/10.1016/j.eswa.2021.114677 (2021). 34313440. The input images were not normalized but the brightness of the images was adjusted through histogram equalization. Are you sure you want to create this branch? The fine-tuning can be trivially accomplished using a holdout/validation set. The performance improvement observed at the X(1)+X(2)+Y(1)+Y(2) position can be attributed to the fact that when the attention module is applied at the X(i)+Y(i)(i{1,2,3,4}) position, the attention map extracted through X(i) is used as the input of the Y(i) attention module. The training loss in Figure 7 also shows that our method with lower loss performs better than the other two methods by combining the texture and deep radiomics features. Download Download PDF. Gusztav et al. We use the above model to evaluate the segmentation performance in the Haut dataset. Scientific Reports (Sci Rep) and L.Y. CXRs are one of the most commonly prescribed medical imaging procedures with the voluminous CXR scans placing significant load on radiologists and medical practitioners. Development of a digital image database for chest radiographs with and without a lung nodule: Receiver operating characteristic analysis of radiologists detection of pulmonary nodules. Traditional lung segmentation methods do not rely on the dataset labeled by professional radiologists, so they are easy to implement. In general, feature maps at shallow layers encode fine details, whereas feature maps at deeper layers carry more global semantic information. Rahul et al.17 used full convolution neural networks to segment the lung field of JSRT and MC datasets, with an average accuracy of 98.92% and 97.84%, respectively. However, extreme lung shape changes and fuzzy lung regions caused by serious lung diseases may incorrectly make the automatic lung segmentation model. One possible reason is that the X- and Y-attention modules learned what and where to emphasize or suppress effectively, enabling them to provide accurate pixel-level attention information. ; investigation, M.K. Finally, we apply a 11 convolution layer and then use the "Sigmoid" activation function to output the mask. But they do not verify the segmentation performance of the model on the benchmark dataset and do not summarize the segmentation scores of different CXR images. Figure4 shows the performance of our lung segmentation model in two benchmark datasets. The Japanese Society of Radiological Technology creates the JSRT dataset15 in collaboration with the JapaneseRadiological Society. A manual Segmentation of OARs and GTV searching with keywords "lung cancer, automatic segmentation, for Lung Cancer and deep learning" was carried out on three academic electronic The pathological characteristics of lung cancer are more complex databases viz. LeCun Y., Bengio Y., Hinton G. Deep learning. Singh, A. et al. We also evaluated lung segmentation of specific illnesses. Several studies have been conducted on lung segmentation using conventional image processing techniques such as edge detection, threshold, and clustering [9]. In image segmentation tasks, especially medical image segmentation, U-Net8 is undoubtedly one of the most successful methods. This multiplication generates the final attention map MXRCHW of the X-attention module through the sigmoid function (), which is an activation function (see Equation (2)). Background: Automated segmentation of anatomical structures is a crucial step in image analysis. Int. Automatic lung segmentation in chest X-ray images using improved U-Net. BackgroundIdentification of lung parenchyma on computer tomographic (CT) scans in the research setting is done semi-automatically and requires cumbersome manual correction. Internet Explorer). 375380. The deeper the network is, the more obvious the "vanishing gradient," and the training effect of the network will not be very good. Chen H, Yu Q, Xie J, Liu S, Pan C, Liu L, Huang Y, Guo F, Qiu H, Yang Y. Crit Care. Method 3: U-net architecture + Efficientnet-b4 encoder + Residual block. The initial learning rate of the model is set to 0.0002. Our network learns to predict binary masks for a given CXR, by learning to discriminate regions of organ, in this case the lungs, from regions of no organ and achieves very realistic and accurate segmentation. Each step consists of different substeps. A survey on deep learning in medical image analysis. Lung segmentation in chest radiographs using anatomical atlases with nonrigid registration. We propose a novel automatic segmentation method using radiomics for ILD patterns from HRCT images. Spinal cord detection in planning CT for radiotherapy through adaptive template matching, IMSLIC and convolutional neural networks. We just have to make sure that while doing this rotation the boundaries of lungs and edges do not go out of the image boundary, Width shift- images are randomly shifted on the horizontal axis by a fraction of total width, Height shift - Images are randomly shifted on the vertical axis by a fraction of the total height. The automatic segmentation of the lung region for chest X-ray (CXR) can help doctors diagnose many lung diseases. The dataset contains 326 normal images and 336 abnormal images showing various manifestations of tuberculosis. Segmentation of pulmonary X-ray computed tomography (CT) images is a precursor to most pulmonary image analysis applications. PMC legacy view Licensee MDPI, Basel, Switzerland. Badrinarayanan, V., Kendall, A. In particular, the saliency transformation function for adding spatial weights to the input image is based on a recurrent neural network. Bildverarbeitung fr die Medizin 2020. https://doi.org/10.1007/978-3-658-29267-6_17 (2020). Among the existing medical imaging methods, X-ray is one of the most commonly used diagnostic technology as it is widely available, low cost, non-invasive, and easy to acquire1,2. An efficient variant of fully-convolutional network for segmenting lung fields from chest radiographs. Qin H., Yang S. X. Adaptive neuro-fuzzy inference systems based approach to nonlinear noise cancellation for images. Five segmentation performance indexes: Accuracy, Sensitivity, Specificity, Dice coefficient, and Jaccard index, are used to evaluate the model. FOIA This article introduces variational auto-encoder (VAE) in each . In the next section, related works on lung image segmentation are introduced. Influence of combined features for segmentation. He, K., Zhang, X., Ren, S., Sun, J. J. I. C. o. C. V. & Recognition, P. Deep residual learning for image recognition. Following are the five segmentation performance metrics we use: accuracy, sensitivity, specificity, dicecoefficient, and Jaccard Index. https://www.kaggle.com/nih-chest-xrays/data, https://github.com/albumentations-team/albumentations, https://doi.org/10.1049/iet-ipr.2016.0526, https://doi.org/10.1016/j.eswa.2021.114677, https://doi.org/10.1016/j.ijid.2014.12.007, https://doi.org/10.1007/s11548-019-01917-1, https://doi.org/10.1007/978-3-319-24574-4_28, https://doi.org/10.1016/j.media.2005.02.002, https://doi.org/10.1186/s12938-018-0544-y, https://doi.org/10.1016/j.cmpb.2019.01.005, https://doi.org/10.1038/s41598-019-51832-3, https://doi.org/10.1007/978-3-319-67558-9_11, https://doi.org/10.2214/ajr.174.1.1740071, https://doi.org/10.1007/s11277-018-5777-3, https://doi.org/10.1007/s11277-018-5702-9, https://doi.org/10.1109/TPAMI.2016.2644615, https://doi.org/10.1109/ICIP.2015.7351179, https://doi.org/10.1007/978-3-319-93000-8_9, https://doi.org/10.1007/978-3-658-29267-6_17, https://doi.org/10.1016/j.cmpb.2019.06.005, https://doi.org/10.1109/ICCE-China.2018.8448537, https://doi.org/10.1109/CVPR.2015.7298965, https://doi.org/10.1007/978-3-030-01234-2_49, https://doi.org/10.1016/j.amc.2019.01.038, https://doi.org/10.1109/JBHI.2017.2687939, https://doi.org/10.1109/EMBC44109.2020.9176033, https://doi.org/10.1016/j.bspc.2021.102666, http://creativecommons.org/licenses/by/4.0/. The inference time was less than 1.4 s per chest X-ray image and the effect of adding attention modules was negligible. Of course, some scholars try to label the NIH Chest X-ray dataset for lung segmentation22. Figure 3 shows an example of computation for GLCM and NGLCM where every cell contains the probability value. It is usually employed to investigate lung nodule measurements, automatic detection, and segmentation. But the lung boundaries obtained may not be optimum due to the heterogeneity of lung field shapes. 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. Various studies on medical imaging using deep learning that combine medical imaging with image classification, detection, and segmentation have been conducted recently [1]. The accuracy of this kind of algorithm is far lower than that of neural network modeling6,9. [(accessed on 21 August 2020)]; Digital Image Database. Xiu J. J., Li Y. X., Cui Y. F. The diagnosis of interstitial lung disease in high resolution CT. Lambin P., Rios-velazquez E., Leijenaar R., et al. We adopt dice similarity coefficient (DSC) [40], sensitivity (SEN) [24], and training time (T, one epoch) as evaluation metrics for the proposing method, defined as follows: where M is the area of ground truth and A is the area of segmentation lung using the proposed method. Proc SPIE Int Soc Opt Eng. Extending the value of routine lung screening CT with quantitative body composition assessment. If nothing happens, download Xcode and try again. The function of the dropout layer is to improve the generalization ability of the model and prevent the model from overfitting. For direction =0, 45, 90, and135, the values of parameters x, y at different are given in Table 1. 1. Our model got an accuracy of 98.9%, 99.3% of specificity, 97.5% sensitivity, 97.7% dice coefficient, and 95.5% Jaccard index for the MC dataset. The abnormality of images is graded from extremely subtle to obvious.