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Objective. Geometrical region of interest (ROI) selection in electrical impedance tomography (EIT) monitoring may lack sensitivity to subtle changes in ventilation distribution. Therefore, we demonstrate a new physiological method for ROI definition. This is relevant when using ROIs to compute subsequent EIT-parameters, such as the ventral-to-dorsal ratio during a positive end-expiratory pressure (PEEP) trial. Approach. Our physiological approach divides an EIT image to ensure exactly 50% tidal impedance variation in the ventral and dorsal region. To demonstrate the effects of our new method, EIT measurements during a decremental PEEP trial in 49 mechanically ventilated ICU-patients were used. We compared the center of ventilation (CoV), a robust parameter for changes in ventro-dorsal ventilation distribution, to our physiological ROI selection method and different commonly used ROI selection methods. Moreover, we determined the impact of different ROI selection methods on the PEEP level corresponding to a ventral-to-dorsal ratio closest to 1. Main results. The division line separating the ventral and dorsal ROI was closer to the CoV for our new physiological method for ROI selection compared to geometrical ROI definition. Moreover, the PEEP level corresponding to a ventral-to-dorsal ratio of 1 is strongly influenced by the chosen ROI selection method, which could have a profound clinical impact; the within-subject range of PEEP level was 6.2 cmH2O depending on the chosen ROI selection method. Significance. Our novel physiological method for ROI definition is sensitive to subtle ventilation-induced changes in regional impedance (i.e. due to (de)recruitment) during mechanical ventilation, similar to the CoV. [ABSTRACT FROM AUTHOR]

Abstract Lung images often have the characteristics of strong noise, uneven grayscale distribution, and complex pathological structures, which makes lung image segmentation a challenging task. To solve this problems, this paper proposes an initial lung mask extraction algorithm that combines threshold and gradient. The gradient used in the algorithm is obtained by the time series feature extraction method based on differential memory (TFDM), which is obtained by the grayscale threshold and image grayscale features. At the same time, we also proposed a lung contour repair algorithm based on the improved convex hull method to solve the contour loss caused by solid nodules and other lesions. Experimental results show that on the COVID-19 CT segmentation dataset, the advanced lung segmentation algorithm proposed in this article achieves better segmentation results and greatly improves the consistency and accuracy of lung segmentation. Our method can obtain more lung information, resulting in ideal segmentation effects with improved accuracy and robustness.

Lung images often have the characteristics of strong noise, uneven grayscale distribution, and complex pathological structures, which makes lung image segmentation a challenging task. To solve this problems, this paper proposes an initial lung mask extraction algorithm that combines threshold and gradient. The gradient used in the algorithm is obtained by the time series feature extraction method based on differential memory (TFDM), which is obtained by the grayscale threshold and image grayscale features. At the same time, we also proposed a lung contour repair algorithm based on the improved convex hull method to solve the contour loss caused by solid nodules and other lesions. Experimental results show that on the COVID-19 CT segmentation dataset, the advanced lung segmentation algorithm proposed in this article achieves better segmentation results and greatly improves the consistency and accuracy of lung segmentation. Our method can obtain more lung information, resulting in ideal segmentation effects with improved accuracy and robustness. [ABSTRACT FROM AUTHOR]

Aim/Background: In this work, a novel method for improving the quality of healthcare in the diagnosis of Interstitial Lung Disease (ILD) using High-Resolution Computed Tomography (HRCT) images is proposed. Materials and Methods: In contrast to previous research that necessitated the human identification of Regions of Interest (ROI), a two-phase deep learning method is presented. First, multi-scale feature extraction is used to precisely segment the lung in HRCT images using a conditional Generative Adversarial Network (c-GAN). A Support Vector Machine (SVM) classifier classifies the characteristics extracted by a pretrained ResNet50 from the segmented lung image into seven ILD classes in the second step. Results: The two-step approach that is being offered improves efficiency by doing away with the necessity for ROI extraction. The superiority of the method is demonstrated by performance comparison with patch-based and whole-image-based algorithms. The suggested method reduces false alarms by achieving a maximum classification accuracy of 94.65% for the normal class. Despite having the lowest accuracy (84.12%), the consolidation class performs better than other whole-image-based methods. Conclusion: The suggested two-stages ILD classifier performs much better due to the step-by-step improvement in the deep learning method. This work lays the groundwork for advanced decision support systems in the pharmaceutical industry and advances pharmaceutical research and education. The method proposed improves knowledge of the pathophysiology of ILD and allows for customized treatment approaches. [ABSTRACT FROM AUTHOR]

This work presents the application of an Encoder-Decoder convolutional neural network (ED-CNN) model to automatically segment COVID-19 computerised tomography (CT) data. By doing so we are producing an alternative model to current literature, which is easy to follow and reproduce, making it more accessible for real-world applications as little training would be required to use this. Our simple approach achieves results comparable to those of previously published studies, which use more complex deep-learning networks. We demonstrate a high-quality automated segmentation prediction of thoracic CT scans that correctly delineates the infected regions of the lungs. This segmentation automation can be used as a tool to speed up the contouring process, either to check manual contouring in place of a peer checking, when not possible or to give a rapid indication of infection to be referred for further treatment, thus saving time and resources. In contrast, manual contouring is a time-consuming process in which a professional would contour each patient one by one to be later checked by another professional. The proposed model uses approximately 49 k parameters while others average over 1,000 times more parameters. As our approach relies on a very compact model, shorter training times are observed, which make it possible to easily retrain the model using other data and potentially afford "personalised medicine" workflows. The model achieves similarity scores of Specificity (Sp) = 0.996 ± 0.001, Accuracy (Acc) = 0.994 ± 0.002 and Mean absolute error (MAE) = 0.0075 ± 0.0005. [ABSTRACT FROM AUTHOR]

Chest x-rays (CXRs) are broadly used in clinical practice to diagnose pulmonary diseases. Developing reliable computer-aided diagnosis (CAD) systems to automate the interpretation of CXRs can save medical practitioners time and improve diagnosis accuracy. Besides, segmenting lungs accurately plays a vital role in CAD systems. However, several challenges hamper the generalizability of current automatic lung segmentation approaches. These include the high varied shape and scale of lungs, the device-specific artifacts in CXRs, and the false positive generation. This study aims to address these issues by proposing a new attention-gated residual deep convolutional network based on an enhanced DeepLabV3 + network. The novel sophistications proposed promote the effective learning of intrinsic lung-related features to improve the network robustness against device-specific artifacts and false positive generation. In the encoder, the proposed framework leverages the attention gate mechanism to selectively filter irrelevant lower-level features using high-level multiscale features learned by the Atrous Spatial Pyramid Pooling (ASPP) module. This improves the network adaptability to different scale and shape lungs and reduces its bias to device-specific artifacts. In the decoder, residual convolutions are used to prevent significant information loss during the segmentation map composition. Several models based on the proposed approach are built by employing the EfficientNetB4 and ResNet50 pretrained models as feature extractors and using different public datasets for training and evaluation. Empirical results demonstrated the proposed framework superiority over many state-of-the-art models by achieving 98.03% for the dice coefficient and 96.14% for the Jaccard index without applying any preprocessing or post-processing. [ABSTRACT FROM AUTHOR]

IntroductionSegmentation of lung structures in medical imaging is crucial for the application of automated post-processing steps on lung diseases like cystic fibrosis (CF). Recently, machine learning methods, particularly neural networks, have demonstrated remarkable improvements, often outperforming conventional segmentation methods. Nonetheless, challenges still remain when attempting to segment various imaging modalities and diseases, especially when the visual characteristics of pathologic findings significantly deviate from healthy tissue.MethodsOur study focuses on imaging of pediatric CF patients [mean age, standard deviation (7.50 ± 4.6)], utilizing deep learning-based methods for automated lung segmentation from chest magnetic resonance imaging (MRI). A total of 165 standardized annual surveillance MRI scans from 84 patients with CF were segmented using the nnU-Net framework. Patient cases represented a range of disease severities and ages. The nnU-Net was trained and evaluated on three MRI sequences (BLADE, VIBE, and HASTE), which are highly relevant for the evaluation of CF induced lung changes. We utilized 40 cases for training per sequence, and tested with 15 cases per sequence, using the Sørensen-Dice-Score, Pearson’s correlation coefficient (r), a segmentation questionnaire, and slice-based analysis.ResultsThe results demonstrated a high level of segmentation performance across all sequences, with only minor differences observed in the mean Dice coefficient: BLADE (0.96 ± 0.05), VIBE (0.96 ± 0.04), and HASTE (0.95 ± 0.05). Additionally, the segmentation quality was consistent across different disease severities, patient ages, and sizes. Manual evaluation identified specific challenges, such as incomplete segmentations near the diaphragm and dorsal regions. Validation on a separate, external dataset of nine toddlers (2–24 months) demonstrated generalizability of the trained model achieving a Dice coefficient of 0.85 ± 0.03.Discussion and conclusionOverall, our study demonstrates the feasibility and effectiveness of using nnU-Net for automated segmentation of lung halves in pediatric CF patients, showing promising directions for advanced image analysis techniques to assist in clinical decision-making and monitoring of CF lung disease progression. Despite these achievements, further improvements are needed to address specific segmentation challenges and enhance generalizability.

Tuberculosis (TB) is a chronic infectious lung disease, which caused the death of about 1.5 million people in 2020 alone. Therefore, it is important to detect TB accurately at an early stage to prevent the infection and associated deaths. Chest X-ray (CXR) is the most popularly used method for TB diagnosis. However, it is difficult to identify TB from CXR images in the early stage, which leads to time-consuming and expensive treatments. Moreover, due to the increase of drug-resistant tuberculosis, the disease becomes more challenging in recent years. In this work, a novel deep learning-based framework is proposed to reliably and automatically distinguish TB, non-TB (other lung infections), and healthy patients using a dataset of 40,000 CXR images. Moreover, a stacking machine learning-based diagnosis of drug-resistant TB using 3037 CXR images of TB patients is implemented. The largest drug-resistant TB dataset will be released to develop a machine learning model for drug-resistant TB detection and stratification. Besides, Score-CAM-based visualization technique was used to make the model interpretable to see where the best performing model learns from in classifying the image. The proposed approach shows an accuracy of 93.32% for the classification of TB, non-TB, and healthy patients on the largest dataset while around 87.48% and 79.59% accuracy for binary classification (drug-resistant vs drug-sensitive TB), and three-class classification (multi-drug resistant (MDR), extreme drug-resistant (XDR), and sensitive TB), respectively, which is the best reported result compared to the literature. The proposed solution can make fast and reliable detection of TB and drug-resistant TB from chest X-rays, which can help in reducing disease complications and spread. [ABSTRACT FROM AUTHOR]

Chest X-ray (CXR) images serve as a fundamental diagnostic tool in the field of medical imaging. Accurate and robust lung segmentation in CXR images is a crucial step toward prediction of age, automating disease diagnosis and monitoring. Deep learning algorithms have been fig used in the segmentation process. The low-resolution output closely resembles the original high-resolution image. However, after being resampled, the image borders become blurred and, in some cases, CXR images may have low contrast between lung tissue and surrounding structures, making it challenging for algorithms to distinguish between them accurately. To overcome the difficulties noted above, a novel approach is introduced for lung segmentation in CXR images, which combines the power of CXR-Autoencoder Segmentation (CXR-AES) with Markov Random Fields (MRF) to achieve enhanced precision and performance. The CXR-AES component is responsible for feature extraction and initial segmentation, while the MRF serves as a contextual model that refines the segmentation results by considering spatial dependencies among pixels. This synergistic fusion of techniques enables the model to capture intricate lung boundaries and handle challenging cases, including pathologies and image artifacts. Finally, the above method gives a good result with dice, sensitivity, specificity, and precision performance metrics of 95.6, 89.9, 99.5, and 91.0% on segmented masks and lung, respectively. CXR-AES with MRF in lung segmentation has broad implications for clinical practice, research, and healthcare innovation. It enhances the efficiency of lung region extraction in CXR images, ultimately improving the diagnosis, treatment, and management of lung-related conditions. [ABSTRACT FROM AUTHOR]

Automatic and precise segmentation of lung images can assist doctors in locating and diagnosing lung lesions. However, current traditional lung CT image lesion segmentation algorithms suffer from the problem of low segmentation accuracy, while deep learning-based segmentation algorithms struggle to strike a better balance between lightweight and high accuracy. In response to this issue, a multi-scale and lightweight U-Net lung image segmentation algorithm with an attention mechanism is proposed. This algorithm introduces CA convolution after the convolution in the encoding stage to extract channel relationships and positional information from the feature maps. Furthermore, the RFB module is employed to extract features from different perspectives. Lastly, upward residual connections are introduced between the RFB modules in the encoder and decoder to enhance inter-network information interaction. Experiments conducted on the LUNA (lung nodule analysis) dataset and the COVID-QU-Ex dataset for COVID-19 pneumonia demonstrate that the proposed MSA-UNet algorithm achieves the best results in terms of Precision and Dice metrics. It outperforms mainstream models such as U-Net++ and DeeplabV3+ in terms of segmentation effectiveness and segmentation generality. The model has a floating-point operation count (FLOPs) of 18.15 G, a network parameter counts of 8.83×106, and achieves a Precision of 99.37%. The algorithm achieves a good balance between computational efficiency, model size, and segmentation accuracy. [ABSTRACT FROM AUTHOR]

Lung segmentation is a prerequisite for lung cancer diagnosis with computer-aided diagnosis systems. However, correct lung segmentation is a challenging task due to image noise, diseases, different lung nodule presences, unique morphological variations, and other factors. In this study, we present a novel algorithm for lung segmentation of thoracic Computed Tomography (CT) images based on a dilated U-Net model and a multi-scale gray correlation-based approach. Lung regions were first extracted from CT images with a double dilated U-Net model for generating accurate lung contours with juxta-pleural nodules included. Then, initial nodule contours were captured using a novel multi-scale gray correlation-based segmentation approach for reducing the computational burden and improving lung segmentation accuracy in lung nodule segmenting. Finally, lung nodule contours were refined with a level set method. A collection of thoracic CT scans with nodules from two public databases are employed for algorithm testing. Experimental results show that the proposed algorithm creates an average Dice similarity coefficient of 72.14% compared with ground truth, and it also outperforms a number of existing lung segmentation techniques. The accurate lung segmentations generated by the proposed algorithm are helpful for assisting radiologists in evaluating lung nodules and subsequently developing focused treatment strategies. [ABSTRACT FROM AUTHOR]

Background: Endobronchial valve placement is a minimally invasive option for treatment of patients with severe emphysema, by reducing lung volumes in lobes with both poor ventilation and perfusion; ventilation is determined by emphysematous scores and perfusion by quantitative lung perfusion imaging. CT‐based fissure identifying artificial intelligence algorithms have recently demonstrated enhanced quantification of the perfusion in a 5‐lobar analysis. We hypothesized that this newly developed algorithm may offer greater utility in determining target treatment lobes by supplementing the radiographic risk stratification initiated by the conventional emphysematous scores alone. Methods: Quantification images of 43 deidentified individuals underwent perfusion SPECT/CT with Tc99m Macro‐Aggregated Albumin (4mCi/148MBq intravenous) using both conventional zonal anatomy and AI augmented 5‐lobar analysis. Analysis: Images were reviewed to demonstrate that the new algorithm was not inferior to standard of care imaging with zonal segmentation. A pilot subcohort analysis of 4 patients with severe emphysema who had pre‐endobronchial valve placement imaging demonstrated that an emphysema‐perfusion ratio greater than 3 was indicative of a potential target lobe. Discussion: We conclude that 5‐lobar analysis in not inferior to conventional zonal analysis and allows the determination of emphysema‐to‐perfusion ratio. Preliminary review of a small subcohort suggests an emphysema‐to‐perfusion ratio greater than 3 for a lobe may clinically benefit in endobronchial valve placement. Further evaluation with prospective studies and larger sample sizes are recommended before clinical implementation. [ABSTRACT FROM AUTHOR]

Background: Hyperpolarized (HP) gas MRI enables the clear visualization of lung structure and function. Clinically relevant biomarkers, such as ventilated defect percentage (VDP) derived from this modality can quantify lung ventilation function. However, long imaging time leads to image quality degradation and causes discomfort to the patients. Although accelerating MRI by undersampling k‐space data is available, accurate reconstruction and segmentation of lung images are quite challenging at high acceleration factors. Purpose: To simultaneously improve the performance of reconstruction and segmentation of pulmonary gas MRI at high acceleration factors by effectively utilizing the complementary information in different tasks. Methods: A complementation‐reinforced network is proposed, which takes the undersampled images as input and outputs both the reconstructed images and the segmentation results of lung ventilation defects. The proposed network comprises a reconstruction branch and a segmentation branch. To effectively exploit the complementary information, several strategies are designed in the proposed network. Firstly, both branches adopt the encoder‐decoder architecture, and their encoders are designed to share convolutional weights for facilitating knowledge transfer. Secondly, a designed feature‐selecting block discriminately feeds shared features into decoders of both branches, which can adaptively pick suitable features for each task. Thirdly, the segmentation branch incorporates the lung mask obtained from the reconstructed images to enhance the accuracy of the segmentation results. Lastly, the proposed network is optimized by a tailored loss function that efficiently combines and balances these two tasks, in order to achieve mutual benefits. Results: Experimental results on the pulmonary HP 129Xe MRI dataset (including 43 healthy subjects and 42 patients) show that the proposed network outperforms state‐of‐the‐art methods at high acceleration factors (4, 5, and 6). The peak signal‐to‐noise ratio (PSNR), structural similarity (SSIM), and Dice score of the proposed network are enhanced to 30.89, 0.875, and 0.892, respectively. Additionally, the VDP obtained from the proposed network has good correlations with that obtained from fully sampled images (r = 0.984). At the highest acceleration factor of 6, the proposed network promotes PSNR, SSIM, and Dice score by 7.79%, 5.39%, and 9.52%, respectively, in comparison to the single‐task models. Conclusion: The proposed method effectively enhances the reconstruction and segmentation performance at high acceleration factors up to 6. It facilitates fast and high‐quality lung imaging and segmentation, and provides valuable support in the clinical diagnosis of lung diseases. [ABSTRACT FROM AUTHOR]

AbstractMedical image segmentation is a crucial stage in computer vision and image processing to help the later-stage diagnosis process become more accurate. Because medical image segmentation, such as X-ray, can extract tissue, organs, and pathological structures. However, medical image processing, primarily in the segmentation process, has significant challenges regarding feature representation. Because medical images have different characteristics than other images related to contrast, blur, and noise. This study proposes the use of lung segmentation on chest X-ray images based on deep learning with the FCA-Net (Fully Convolutional Attention Network) architecture. In addition, attention modules, namely spatial attention and channel attention, are added to the Res2Net encoder so that it is expected to be able to represent features better. This research was conducted on chest X-ray images from Qatar University contained in the Kaggle repository. A chest x-ray image measuring 256 × 256 pixels and as many as 1500 images were then divided into 10% testing data and 90% training data. The training data will then be processed in K-Fold Cross validation from K = 2 until K = 10. The experiment was conducted with scenarios that used spatial attention, channel attention, and a combination of spatial and channel attention. The best test results in this study were using a variety of spatial attention and channel attention in the division of K-Fold with a value of K = 5 with a DSC (Dice Similarity Coefficient) value in the testing data of 97.24% and IoU (Intersection over Union) in the testing data of 94.66%. This accuracy result is better than the UNet++, DeepLabV3+, and SegNet architectures.

With the advances in technology, assistive medical systems are emerging with rapid growth and helping healthcare professionals. The proactive diagnosis of diseases with artificial intelligence (AI) and its aligned technologies has been an exciting research area in the last decade. Doctors usually detect tuberculosis (TB) by checking the lungs' X-rays. Classification using deep learning algorithms is successfully able to achieve accuracy almost similar to a doctor in detecting TB. It is found that the probability of detecting TB increases if classification algorithms are implemented on segmented lungs instead of the whole X-ray. The paper's novelty lies in detailed analysis and discussion of U-Net + + results and implementation of U-Net + + in lung segmentation using X-ray. A thorough comparison of U-Net + + with three other benchmark segmentation architectures and segmentation in diagnosing TB or other pulmonary lung diseases is also made in this paper. To the best of our knowledge, no prior research tried to implement U-Net + + for lung segmentation. Most of the papers did not even use segmentation before classification, which causes data leakage. Very few used segmentations before classification, but they only used U-Net, which U-Net + + can easily replace because accuracy and mean_iou of U-Net + + are greater than U-Net accuracy and mean_iou , discussed in results, which can minimize data leakage. The authors achieved more than 98% lung segmentation accuracy and mean_iou 0.95 using U-Net + + , and the efficacy of such comparative analysis is validated. [ABSTRACT FROM AUTHOR]

Segmentation of lung regions is of key importance for the automatic analysis of Chest X-Ray (CXR) images, which have a vital role in the detection of various pulmonary diseases. Precise identification of lung regions is the basic prerequisite for disease diagnosis and treatment planning. However, achieving precise lung segmentation poses significant challenges due to factors such as variations in anatomical shape and size, the presence of strong edges at the rib cage and clavicle, and overlapping anatomical structures resulting from diverse diseases. Although commonly considered as the de-facto standard in medical image segmentation, the convolutional UNet architecture and its variants fall short in addressing these challenges, primarily due to the limited ability to model long-range dependencies between image features. While vision transformers equipped with self-attention mechanisms excel at capturing long-range relationships, either a coarse-grained global self-attention or a fine-grained local self-attention is typically adopted for segmentation tasks on high-resolution images to alleviate quadratic computational cost at the expense of performance loss. This paper introduces a focal modulation UNet model (FMN-UNet) to enhance segmentation performance by effectively aggregating fine-grained local and coarse-grained global relations at a reasonable computational cost. FMN-UNet first encodes CXR images via a convolutional encoder to suppress background regions and extract latent feature maps at a relatively modest resolution. FMN-UNet then leverages global and local attention mechanisms to model contextual relationships across the images. These contextual feature maps are convolutionally decoded to produce segmentation masks. The segmentation performance of FMN-UNet is compared against state-of-the-art methods on three public CXR datasets (JSRT, Montgomery, and Shenzhen). Experiments in each dataset demonstrate the superior performance of FMN-UNet against baselines. [ABSTRACT FROM AUTHOR]

Convolutional neural networks (CNNs) are constantly being used for medical image processing with increased application in publicly available datasets and are later being actively applied in medical practice. Therefore, since patient lives are at stake, it is important that the functionality of the neural network is beyond reproach. In this paper, due to dataset availability, we present two lung segmentation approaches using traditional image processing and deep learning methodologies; these approaches can later be used to focus a CNN for image segmentation and classification tasks, with implementations spanning everything from disease diagnosis to demographic and bias analysis. The aim of this paper is to provide a framework for segmentation in medical images of the chest cavity, as a way of applying attention regions and localizing sources of bias in images. Both of the proposed segmentation tools, the traditional image approach using computer tomography scans and the CNN applied to chest X-rays, provide excellent lung segmentation comparable to popular methods in the image processing sphere. This allows for an all-encompassing application of the developed methodology regardless of different image formats, therefore making it widely applicable in setting attention regions for CNNs.

Bronchiectasis in children is a major health issue which can be life-threatening if not diagnosed and effectively treated. In the diagnosis of bronchiectasis, an increased broncho-arterial (BA) ratio is considered a significant marker. The BA ratio is measured by evaluating BA pairs, using high-resolution computed tomography (HRCT) scans. Detecting BA pairs automatically is challenging due to the complex characteristics of BA pairs and the ambiguous appearance of the bronchi. This study proposes an effective computerized approach to detect BA pairs and assess BA ratio using HRCT scans of children and employing computer-aided techniques and novel custom-build algorithms. Attention is given to reconstructing broken bronchial walls and identifying discrete BA pairs using custom-built kernel based and patch-based algorithms for pixel-level image analysis. To detect BA pairs, the lung region is segmented in the HRCT slices and image preprocessing techniques, including noise reduction, binarizing, largest contour detection and a hole-filling algorithm, are applied. A histogram analysis method is introduced to clean the images. A kernel-based algorithm is proposed to reconstruct the pixel distribution if the bronchial wall is so that the bronchi can be detected precisely. Potential arteries are detected using balanced histogram thresholding, morphological opening and an approach based on four conditions related to the object area circularity, rectangular boundary box ratio and enclosing circle area ratio. Potential bronchi are detected through matching of object coordinates with potential arteries, hole-filling and four condition based approaches. The potential BA pairs are detected by matching the coordinates of potential bronchi with those of potential arteries as the artery and bronchus are adjacent to each other in BA pairs. Finally, from the potential BA pairs, actual BA pairs are identified using a custom-built patch algorithm. The study is conducted using 2471 HRCT slices of seven children, obtained from the Royal Darwin Hospital, Australia. The BA ratio is derived based on the ratio of diameters, major axis lengths, minor axis lengths, area, convex hull and equivalent diameter where the BA ratios are respectively 0.51–0.65, 0.49–0.59, 0.59–0.77, 0.25–0.42, 0.29–0.47, 1.5–2 and 0.50–0.65.

Lung segmentation algorithms play a significant role in segmenting theinfected regions in the lungs. This work aims to develop a computationally efficient and robust deep learning model for lung segmentation using chest computed tomography (CT) images with DeepLabV3 + networks for two-class (background and lung field) and four-class (ground-glass opacities, background, consolidation, and lung field). In this work, we investigate the performance of the DeepLabV3 + network with five pretrained networks: Xception, ResNet-18, Inception-ResNet-v2, MobileNet-v2 and ResNet-50. A publicly available database for COVID-19 that contains 750 chest CT images and corresponding pixel-labeled images are used to develop the deep learning model. The segmentation performance has been assessed using five performance measures: Intersection of Union (IoU), Weighted IoU, Balance F1 score, pixel accu-racy, and global accuracy. The experimental results of this work confirm that the DeepLabV3 + network with ResNet-18 and a batch size of 8 have a higher performance for two-class segmentation. DeepLabV3 + network coupled with ResNet-50 and a batch size of 16 yielded better results for four-class segmentation compared to other pretrained networks. Besides, the ResNet with a fewer number of layers is highly adequate for developing a more robust lung segmentation network with lesser computational complexity compared to the conventional DeepLabV3 + network with Xception. This present work proposes a unified DeepLabV3 + network to delineate the two and four different regions automatically using CT images for CoVID-19 patients. Our developed automated segmented model can be further developed to be used as a clinical diagnosis system for CoVID-19 as well as assist clinicians in providing an accurate second opinion CoVID-19 diagnosis. [ABSTRACT FROM AUTHOR]

COVID-19 has expanded overall across the globe after its initial cases were discovered in December 2019 in Wuhan—China. Because the virus has impacted people's health worldwide, its fast identification is essential for preventing disease spread and reducing mortality rates. The reverse transcription polymerase chain reaction (RT-PCR) is the primary leading method for detecting COVID-19 disease; it has high costs and long turnaround times. Hence, quick and easy-to-use innovative diagnostic instruments are required. According to a new study, COVID-19 is linked to discoveries in chest X-ray pictures. The suggested approach includes a stage of pre-processing with lung segmentation, removing the surroundings that do not provide information pertinent to the task and may result in biased results. The InceptionV3 and U-Net deep learning models used in this work process the X-ray photo and classifies them as COVID-19 negative or positive. The CNN model that uses a transfer learning approach was trained. Finally, the findings are analyzed and interpreted through different examples. The obtained COVID-19 detection accuracy is around 99% for the best models. [ABSTRACT FROM AUTHOR]

Automatic lung segmentation in the chest x‐ray is important for computer aided diagnosis. It helps in the surgical planning and diagnosis of pulmonary diseases. Lung shape, size, overlapped area, and opacities make lung segmentation arduous. In this article, we have proposed a UNet‐based model for lung segmentation. We have evaluated the model on difficult datasets that have chest radiographs of patients affected by tuberculosis and other severe abnormalities. Three chest radiography datasets and a CT‐scan dataset are used to prove the model generalization. The proposed model efficiently uses the residual learning and attention mechanisms to improve the segmentation results against the original UNet for the dice coefficient index (DCI) and Jaccard index. We have also performed an ablation study to highlight the impact of the attention mechanism in the proposed model. The model obtained a 97.62% DCI, 95.43% Jaccard index, and a 4.00 Hausdorff distance on the Montgomery County dataset. While on the Shenzhen and NIH datasets, it achieved a 95.71% and 95.75% DCI, 91.90% and 91.95% Jaccard index, and a 5.23 and 5.20 Hausdorff distance, respectively. The proposed model has achieved better or comparable performance against other state‐of‐the‐art models. [ABSTRACT FROM AUTHOR]

Aim: The main objective of this work is to propose an efficient segmentation model for accurate and robust lung segmentation from computed tomography (CT) images, even when the lung contains abnormalities such as juxtapleural nodules, cavities, and consolidation. Methodology: A novel deep learning-based segmentation model, pyramid-dilated dense U-Net (PDD-U-Net), is proposed to directly segment lung regions from the whole CT image. The model is integrated with pyramid-dilated convolution blocks to capture and preserve multi-resolution spatial features effectively. In addition, shallow and deeper stream features are embedded in the nested U-Net structure at the decoder side to enhance the segmented output. The effect of three loss functions is investigated in this paper, as the medical image analysis method requires precise boundaries. The proposed PDD-U-Net model with shape-aware loss function is tested on the lung CT segmentation challenge (LCTSC) dataset with standard lung CT images and the lung image database consortium-image database resource initiative (LIDC-IDRI) dataset containing both typical and pathological lung CT images. Results: The performance of the proposed method is evaluated using Intersection over Union, dice coefficient, precision, recall, and average Hausdorff distance metrics. Segmentation results showed that the proposed PDD-U-Net model outperformed other segmentation methods and achieved a 0.983 dice coefficient for the LIDC-IDRI dataset and a 0.994 dice coefficient for the LCTSC dataset. Conclusions: The proposed PDD-U-Net model with shape-aware loss function is an effective and accurate method for lung segmentation from CT images, even in the presence of abnormalities such as cavities, consolidation, and nodules. The model's integration of pyramid-dilated convolution blocks and nested U-Net structure at the decoder side, along with shape-aware loss function, contributed to its high segmentation accuracy. This method could have significant implications for the computer-aided diagnosis system, allowing for quick and accurate analysis of lung regions.

Lung segmentation plays an important role in computer-aided detection and diagnosis using chest radiographs (CRs). Currently, the U-Net and DeepLabv3+ convolutional neural network architectures are widely used to perform CR lung segmentation. To boost performance, ensemble methods are often used, whereby probability map outputs from several networks operating on the same input image are averaged. However, not all networks perform adequately for any specific patient image, even if the average network performance is good. To address this, we present a novel multi-network ensemble method that employs a selector network. The selector network evaluates the segmentation outputs from several networks; on a case-by-case basis, it selects which outputs are fused to form the final segmentation for that patient. Our candidate lung segmentation networks include U-Net, with five different encoder depths, and DeepLabv3+, with two different backbone networks (ResNet50 and ResNet18). Our selector network is a ResNet18 image classifier. We perform all training using the publicly available Shenzhen CR dataset. Performance testing is carried out with two independent publicly available CR datasets, namely, Montgomery County (MC) and Japanese Society of Radiological Technology (JSRT). Intersection-over-Union scores for the proposed approach are 13% higher than the standard averaging ensemble method on MC and 5% better on JSRT.

The conventional approach for identifying ground glass opacities (GGO) in medical imaging is to use a convolutional neural network (CNN), a subset of artificial intelligence, which provides promising performance in COVID‐19 detection. However, CNN is still limited in capturing structured relationships of GGO as the texture and shape of the GGO can be confused with other structures in the image. In this paper, a novel framework called DeepChestNet is proposed that leverages structured relationships by jointly performing segmentation and classification on the lung, pulmonary lobe, and GGO, leading to enhanced detection of COVID‐19 with findings. The performance of DeepChestNet in terms of dice similarity coefficient is 99.35%, 99.73%, and 97.89% for the lung, pulmonary lobe, and GGO segmentation, respectively. The experimental investigations on DeepChestNet‐Lung, DeepChestNet‐Lobe and DeepChestNet‐COVID datasets, and comparison with several state‐of‐the‐art approaches reveal the great potential of DeepChestNet for diagnosis of COVID‐19 disease. [ABSTRACT FROM AUTHOR]

Treatment of non‐small cell lung cancer depends on detecting the cancer stage. The oncologist decides the cancer stage based on the tumour‐node‐metastasis (TNM) staging suggested by the American Joint Committee on Cancer (AJCC). This study simplifies the complicated problem of classifying computed tomography (CT) images into TNM‐based classes using deep learning algorithms at various levels. In the first level, an optimised conditional generative adversarial network (c‐GAN) network is developed for automatic lung segmentation, including nodules within the lung and juxtapleural nodules. Earlier studies used time‐consuming manual identification of the region of interest patches from the lung CT image before applying the deep learning classification algorithm. At the next level, three different deep learning algorithms, along with three support vector machine classifiers, are used for the classification of Tumour, Node and Metastasis as per the AJCC staging nomenclature. The specially designed c‐GAN network's performance is maximised using the Taguchi approach, which helps automatically preprocess CT images by removing unwanted background noises. Further, three different pre‐trained Resnet50 networks are trained using transfer learning for extracting the deep features for finally applying to three different classifiers, resulting in three different classes. The comparative segmentation performance assessment in the form of the average dice similarity coefficient and Jaccard index indicates that the proposed c‐GAN gives the best segmentation performance of the lung without losing the nodule compared to other segmentation algorithms. The proposed approach gives the classification performance for the Tumour as 91.94%–97.32%, the Nodule as 91.99%–100%, and the Metastasis as 99.25%–100.00%. [ABSTRACT FROM AUTHOR]

Lung segmentation, a prerequisite step of lung disease detection in computer‐aided diagnosis system, is a challenging task because of noises, complex structures, as well as large individual differences of lung CT scans. Here, an automatic algorithm for segmenting lungs from thoracic CT images accurately is presented. This scheme consists of three principal steps: image preprocessing, lung extracting and contour correcting. To cope with inhomogeneous intensities of CT images, a novel preprocessing approach based on empirical mode decomposition and bilateral filter is proposed, which has abilities of denoising, smoothing and edge keeping. Lung region is then extracted with a novel gray correlation‐based clustering approach. A new lung contour correction technology is finally employed to repair the concave regions caused by pulmonary nodules, vessels and so on. Experimental results show that the preprocessing approach outperforms other methods on image denoising and smoothing. Meanwhile, the lung segmentation algorithm is tested on a group of lung CT images affected with interstitial lung diseases and achieves a high segmentation accuracy. Compared with several existing lung segmentation methods, this algorithm exhibits a better performance on lung segmentation. [ABSTRACT FROM AUTHOR]

Aim: The main objective of this work is to propose an efficient segmentation model for accurate and robust lung segmentation from computed tomography (CT) images, even when the lung contains abnormalities such as juxtapleural nodules, cavities, and consolidation. Methodology: A novel deep learning‑based segmentation model, pyramid‑dilated dense U‑Net (PDD‑U‑Net), is proposed to directly segment lung regions from the whole CT image. The model is integrated with pyramid‑dilated convolution blocks to capture and preserve multi‑resolution spatial features effectively. In addition, shallow and deeper stream features are embedded in the nested U‑Net structure at the decoder side to enhance the segmented output. The effect of three loss functions is investigated in this paper, as the medical image analysis method requires precise boundaries. The proposed PDD‑U‑Net model with shape‑aware loss function is tested on the lung CT segmentation challenge (LCTSC) dataset with standard lung CT images and the lung image database consortium‑image database resource initiative (LIDC‑IDRI) dataset containing both typical and pathological lung CT images. Results: The performance of the proposed method is evaluated using Intersection over Union, dice coefficient, precision, recall, and average Hausdorff distance metrics. Segmentation results showed that the proposed PDD‑U‑Net model outperformed other segmentation methods and achieved a 0.983 dice coefficient for the LIDC‑IDRI dataset and a 0.994 dice coefficient for the LCTSC dataset. Conclusions: The proposed PDD‑U‑Net model with shape‑aware loss function is an effective and accurate method for lung segmentation from CT images, even in the presence of abnormalities such as cavities, consolidation, and nodules. The model’s integration of pyramid‑dilated convolution blocks and nested U‑Net structure at the decoder side, along with shape‑aware loss function, contributed to its high segmentation accuracy. This method could have significant implications for the computer‑aided diagnosis system, allowing for quick and accurate analysis of lung regions. [ABSTRACT FROM AUTHOR]

Purpose: Lung cancer can evolve into one of the deadliest diseases whose early detection is one of the major survival factors. However, early detection is a challenging task due to the unclear structure, shape, and the size of the nodule. Hence, radiologists need automated tools to make accurate decisions. Methods: This paper develops a new approach based on generative adversarial network (GAN) architecture for nodule detection to propose a two-step GAN model containing lung segmentation and nodule localization. The first generator comprises a U-net network, while the second utilizes a mask R-CNN. The task of lung segmentation involves a two-class classification of the pixels in each image, categorizing lung pixels in one class and the rest in the other. The classifier becomes imbalanced due to numerous non-lung pixels, decreasing the model performance. This problem is resolved by using the focal loss function for training the generator. Moreover, a new loss function is developed as the nodule localization generator to enhance the diagnosis quality. Discriminator nets are implemented in GANs as an ensemble of convolutional neural networks (ECNNs), using multiple CNNs and connecting their outputs to make a final decision. Results: Several experiments are designed to assess the model on the well-known LUNA dataset. The experiments indicate that the proposed model can reduce the error of the state-of-the-art models on the IoU criterion by about 35 and 16% for lung segmentation and nodule localization, respectively. Conclusion: Unlike recent studies, the proposed method considers two loss functions for generators, further promoting the goal achievements. Moreover, the network of discriminators is regarded as ECNNs, generating rich features for decisions. [ABSTRACT FROM AUTHOR]

Chest radiography (X-ray) is the most common diagnostic method for pulmonary disorders. A trained radiologist is required for interpreting the radiographs. But sometimes, even experienced radiologists can misinterpret the findings. This leads to the need for computer-aided detection diagnosis. For decades, researchers were automatically detecting pulmonary disorders using the traditional computer vision (CV) methods. Now the availability of large annotated datasets and computing hardware has made it possible for deep learning to dominate the area. It is now the modus operandi for feature extraction, segmentation, detection, and classification tasks in medical imaging analysis. This paper focuses on the research conducted using chest X-rays for the lung segmentation and detection/classification of pulmonary disorders on publicly available datasets. The studies performed using the Generative Adversarial Network (GAN) models for segmentation and classification on chest X-rays are also included in this study. GAN has gained the interest of the CV community as it can help with medical data scarcity. In this study, we have also included the research conducted before the popularity of deep learning models to have a clear picture of the field. Many surveys have been published, but none of them is dedicated to chest X-rays. This study will help the readers to know about the existing techniques, approaches, and their significance. [ABSTRACT FROM AUTHOR]

The Coronavirus Pandemic triggered by SARS-CoV-2 has wreaked havoc on the planet and is expanding exponentially. While scanning methods, including CT scans and chest X-rays, are commonly used, artificial intelligence implementations are also deployed for COVID-based pneumonia detection. Due to image biases in X-ray data, bilateral filtration and Histogram Equalization are used followed by lung segmentation by a U-Net, which successfully segmented 83.2\% of the collected dataset. The segmented lungs are fed into a Quadruplet Network with SqueezeNet encoders for increased computational efficiency and high-level embeddings generation. The embeddings are computed using a Multi-Layer Perceptron and visualized by T-SNE (T-Distributed Stochastic Neighbor Embedding) scatterplots. The proposed research results in a 94.6\% classifying accuracy which is 2\% more than the baseline Convolutional Neural Network and a 90.2\% decrease in prediction time.

BackgroundChest 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.ObjectiveIn 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.MethodsThe 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.ResultsApplied on the test set containing multi-center data, our model achieved a Dice score of 0.9822.ConclusionsThis novel lung segmentation method proposed in this work based on TransResUNet is better than other existing medical image segmentation networks.

Medical image segmentation is a crucial stage in computer vision and image processing to help the later-stage diagnosis process become more accurate. Because medical image segmentation, such as X-ray, can extract tissue, organs, and pathological structures. However, medical image processing, primarily in the segmentation process, has significant challenges regarding feature representation. Because medical images have different characteristics than other images related to contrast, blur, and noise. This study proposes the use of lung segmentation on chest X-ray images based on deep learning with the FCA-Net (Fully Convolutional Attention Network) architecture. In addition, attention modules, namely spatial attention and channel attention, are added to the Res2Net encoder so that it is expected to be able to represent features better. This research was conducted on chest X-ray images from Qatar University contained in the Kaggle repository. A chest x-ray image measuring 256 × 256 pixels and as many as 1500 images were then divided into 10% testing data and 90% training data. The training data will then be processed in K-Fold Cross validation from K = 2 until K = 10. The experiment was conducted with scenarios that used spatial attention, channel attention, and a combination of spatial and channel attention. The best test results in this study were using a variety of spatial attention and channel attention in the division of K-Fold with a value of K = 5 with a DSC (Dice Similarity Coefficient) value in the testing data of 97.24% and IoU (Intersection over Union) in the testing data of 94.66%. This accuracy result is better than the UNet++, DeepLabV3+, and SegNet architectures. [ABSTRACT FROM AUTHOR]

Current research indicates that for the identification of lung disorders, comprising pneumonia and COVID-19, structural distortions of bronchi and arteries (BA) should be taken into account. CT scans are an effective modality to detect lung anomalies. However, anomalies in bronchi and arteries can be difficult to detect. Therefore, in this study, alterations of bronchi and arteries are considered in the classification of lung diseases. Four approaches to highlight these are introduced: (a) a Hessian-based approach, (b) a region-growing algorithm, (c) a clustering-based approach, and (d) a color-coding-based approach. Prior to this, the lungs are segmented, employing several image preprocessing algorithms. The utilized COVID-19 Lung CT scan dataset contains three classes named Non-COVID, COVID, and community-acquired pneumonia, having 6983, 7593, and 2618 samples, respectively. To classify the CT scans into three classes, two deep learning architectures, (a) a convolutional neural network (CNN) and (b) a CNN with long short-term memory (LSTM) and an attention mechanism, are considered. Both these models are trained with the four datasets achieved from the four approaches. Results show that the CNN model achieved test accuracies of 88.52%, 87.14%, 92.36%, and 95.84% for the Hessian, the region-growing, the color-coding, and the clustering-based approaches, respectively. The CNN with LSTM and an attention mechanism model results in an increase in overall accuracy for all approaches with an 89.61%, 88.28%, 94.61%, and 97.12% test accuracy for the Hessian, region-growing, color-coding, and clustering-based approaches, respectively. To assess overfitting, the accuracy and loss curves and k-fold cross-validation technique are employed. The Hessian-based and region-growing algorithm-based approaches produced nearly equivalent outcomes. Our proposed method outperforms state-of-the-art studies, indicating that it may be worthwhile to pay more attention to BA features in lung disease classification based on CT images. [ABSTRACT FROM AUTHOR]

Lung segmentation plays an important role in computer-aided detection and diagnosis using chest radiographs (CRs). Currently, the U-Net and DeepLabv3+ convolutional neural network architectures are widely used to perform CR lung segmentation. To boost performance, ensemble methods are often used, whereby probability map outputs from several networks operating on the same input image are averaged. However, not all networks perform adequately for any specific patient image, even if the average network performance is good. To address this, we present a novel multi-network ensemble method that employs a selector network. The selector network evaluates the segmentation outputs from several networks; on a case-by-case basis, it selects which outputs are fused to form the final segmentation for that patient. Our candidate lung segmentation networks include U-Net, with five different encoder depths, and DeepLabv3+, with two different backbone networks (ResNet50 and ResNet18). Our selector network is a ResNet18 image classifier. We perform all training using the publicly available Shenzhen CR dataset. Performance testing is carried out with two independent publicly available CR datasets, namely, Montgomery County (MC) and Japanese Society of Radiological Technology (JSRT). Intersection-over-Union scores for the proposed approach are 13% higher than the standard averaging ensemble method on MC and 5% better on JSRT. [ABSTRACT FROM AUTHOR]

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