Lung Boundary Research Articles

Pre-operative lung ablation prediction using deep learning.

Microwave lung ablation (MWA) is a minimally invasive and inexpensive alternative cancer treatment for patients who are not candidates for surgery/radiotherapy. However, a major challenge for MWA is its relatively high tumor recurrence rates, due to incomplete treatment as a result of inaccurate planning. We introduce a patient-specific, deep-learning model to accurately predict post-treatment ablation zones toaidplanning and enable effective treatments. Our IRB-approved retrospective study consisted of ablations with a single applicator/burn/vendor between 01/2015 and 01/2019. The input data included pre-procedure computerized tomography (CT), ablation power/time, and applicator position. The ground truth ablation zone was segmented from follow-up CT post-treatment. Novel deformable image registration optimized for ablation scans and an applicator-centric co-ordinate system for data analysis were applied. Our prediction model was based on the U-net architecture. The registrations were evaluated using target registration error (TRE) and predictions using Bland-Altman plots, Dice co-efficient, precision, and recall, compared against the applicator vendor's estimates. The data included 113 unique ablations from 72 patients (median age 57, interquartile range (IQR) (49-67); 41 women). We obtained a TRE ≤ 2 mm on 52 ablations. Our prediction had no bias from ground truth ablation volumes (p = 0.169) unlike the vendor's estimate (p < 0.001) and had smaller limits of agreement (p < 0.001). An 11% improvement was achieved in the Dice score. The ability to account for patient-specific in-vivo anatomical effects due to vessels, chest wall, heart, lung boundaries, and fissures was shown. We demonstrated a patient-specific deep-learning model to predict the ablation treatment effect prior to the procedure, with the potential for improved planning, achievingcomplete treatments, and reduce tumor recurrence. Our method addresses the current lack of reliable tools to estimate ablation extents, required for ensuring successful ablation treatments. The potential clinical implications include improved treatment planning, ensuring complete treatments, and reducing tumor recurrence.

Multi-task localization of the hemidiaphragms and lung segmentation in portable chest X-ray images of COVID-19 patients.

The COVID-19 can cause long-term symptoms in the patients after they overcome the disease. Given that this disease mainly damages the respiratory system, these symptoms are often related with breathing problems that can be caused by an affected diaphragm. The diaphragmatic function can be assessed with imaging modalities like computerized tomography or chest X-ray. However, this process must be performed by expert clinicians with manual visual inspection. Moreover, during the pandemic, the clinicians were asked to prioritize the use of portable devices, preventing the risk of cross-contamination. Nevertheless, the captures of these devices are of a lower quality. The automatic quantification of the diaphragmatic function can determine the damage of COVID-19 on each patient and assess their evolution during the recovery period, a task that could also be complemented with the lung segmentation. We propose a novel multi-task fully automatic methodology to simultaneously localize the position of the hemidiaphragms and to segment the lung boundaries with a convolutional architecture using portable chest X-ray images of COVID-19 patients. For that aim, the hemidiaphragms' landmarks are located adapting the paradigm of heatmap regression. The methodology is exhaustively validated with four analyses, achieving an 82.31% 2.78% of accuracy when localizing the hemidiaphragms' landmarks and a Dice score of 0.9688 0.0012 in lung segmentation. The results demonstrate that the model is able to perform both tasks simultaneously, being a helpful tool for clinicians despite the lower quality of the portable chest X-ray images.

Machine learning modeling of lung mechanics: Assessing the variability and propagation of uncertainty in respiratory-system compliance and airway resistance

Background and ObjectiveTraditional assessment of patient response in mechanical ventilation relies on respiratory-system compliance and airway resistance. Clinical evidence has shown high variability in these parameters, highlighting the difficulty of predicting them before the start of ventilation therapy. This motivates the creation of computational models that can connect structural and tissue features with lung mechanics. In this work, we leverage machine learning (ML) techniques to construct predictive lung function models informed by non-linear finite element simulations, and use them to investigate the propagation of uncertainty in the lung mechanical response. MethodsWe revisit a continuum poromechanical formulation of the lungs suitable for determining patient response. Based on this framework, we create high-fidelity finite element models of human lungs from medical images. We also develop a low-fidelity model based on an idealized sphere geometry. We then use these models to train and validate three ML architectures: single-fidelity and multi-fidelity Gaussian process regression, and artificial neural networks. We use the best predictive ML model to further study the sensitivity of lung response to variations in tissue structural parameters and boundary conditions via sensitivity analysis and forward uncertainty quantification. Codes are available for download at https://github.com/comp-medicine-uc/ML-lung-mechanics-UQ ResultsThe low-fidelity model delivers a lung response very close to that predicted by high-fidelity simulations and at a fraction of the computational time. Regarding the trained ML models, the multi-fidelity GP model consistently delivers better accuracy than the single-fidelity GP and neural network models in estimating respiratory-system compliance and resistance (R2∼0.99). In terms of computational efficiency, our ML model delivers a massive speed-up of ∼970,000× with respect to high-fidelity simulations. Regarding lung function, we observed an almost matched and non-linear behavior between specific structural parameters and chest wall stiffness with compliance. Also, we observed a strong modulation of airways resistance with tissue permeability. ConclusionsOur findings unveil the relevance of specific lung tissue parameters and boundary conditions in the respiratory-system response. Furthermore, we highlight the advantages of adopting a multi-fidelity ML approach that combines data from different levels to yield accurate and efficient estimates of clinical mechanical markers. We envision that the methods presented here can open the way to the development of predictive ML models of the lung response that can inform clinical decisions.

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

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

Joint classification and segmentation for an interpretable diagnosis of acute respiratory distress syndrome from chest x-rays.

Acute respiratory distress syndrome (ARDS) is a life-threatening condition that can cause a dramatic drop in blood oxygen levels due to widespread lung inflammation. Chest radiography is widely used as a primary modality to detect ARDS due to its crucial role in diagnosing the syndrome, and the x-ray images can be obtained promptly. However, despite the extensive literature on chest x-ray (CXR) image analysis, there is limited research on ARDS diagnosis due to the scarcity of ARDS-labeled datasets. Additionally, many machine learning-based approaches result in high performance in pulmonary disease diagnosis, but their decisions are often not easily interpretable, which can hinder their clinical acceptance. This work aims to develop a method for detecting signs of ARDS in CXR images that can be clinically interpretable. To achieve this goal, an ARDS-labeled dataset of chest radiography images is gathered and annotated for training and evaluation of the proposed approach. The proposed deep classification-segmentation model, Dense-Ynet, provides an interpretable framework for automatically diagnosing ARDS in CXR images. The model takes advantage of lung segmentation in diagnosing ARDS. By definition, ARDS causes bilateral diffuse infiltrates throughout the lungs. To consider the local involvement of lung areas, each lung is divided into upper and lower halves, and our model classifies the resulting lung quadrants. The quadrant-based classification strategy yields the area under the receiver operating characteristic curve of 95.1% (95% CI 93.5 to 96.1), which allows for providing a reference for the model's predictions. In terms of segmentation, the model accurately identifies lung regions in CXR images even when lung boundaries are unclear in abnormal images. This study provides an interpretable decision system for diagnosing ARDS, by following the definition used by clinicians for the diagnosis of ARDS from CXR images.

A new segment method for pulmonary artery and vein.

Accurate differentiation between pulmonary arteries and veins (A/V) holds pivotal importance in the realm of diagnosing and treating pulmonary ailments. This study presents a new approach that leverages grayscale differences between A/V. Distinctions are measured using median and mean grayscale values within the vessel area. Initially, adherent regions are removed based on vessel structure. The trunk regions are segmented using gray level information near the heart region of the lung boundary. Incorrectly segmented vessels are corrected based on connectivity. For distal lung vessels, a similar distance field is established using a graph-cut method. Experimental results show the algorithm's superior segmentation accuracy, achieving 97.26% compared to the CNN-based average accuracy of 91.67%. Error branches are more concentrated, aiding subsequent manual and automatic correction. This demonstrates the algorithm's effective segmentation of pulmonary A/V.

Fully automatic deep learning-based lung parenchyma segmentation and boundary correction in thoracic CT scans.

The proposed work aims to develop an algorithm to precisely segment the lung parenchyma in thoracic CT scans. To achieve this goal, the proposed technique utilized a combination of deep learning and traditional image processing algorithms. The initial step utilized a trained convolutional neural network (CNN) to generate preliminary lung masks, followed by the proposed post-processing algorithm for lung boundary correction. First, the proposed method trained an improved 2D U-Net CNN model with Inception-ResNet-v2 as its backbone. The model was trained on 32 CT scans from two different sources: one from the VESSEL12 grand challenge and the other from AIIMS Delhi. Further, the model's performance was evaluated on a test dataset of 16 CT scans with juxta-pleural nodules obtained from AIIMS Delhi and the LUNA16 challenge. The model's performance was assessed using evaluation metrics such as average volumetric dice coefficient (DSCavg), average IoU score (IoUavg), and average F1 score (F1avg). Finally, the proposed post-processing algorithm was implemented to eliminate false positives from the model's prediction and to include juxta-pleural nodules in the final lung masks. The trained model reported a DSCavg of 0.9791 ± 0.008, IoUavg of 0.9624 ± 0.007, and F1avg of 0.9792 ± 0.004 on the test dataset. Applying the post-processing algorithm to the predicted lung masks obtained a DSCavg of 0.9713 ± 0.007, IoUavg of 0.9486 ± 0.007, and F1avg of 0.9701 ± 0.008. The post-processing algorithm successfully included juxta-pleural nodules in the final lung mask. Using a CNN model, the proposed method for lung parenchyma segmentation produced precise segmentation results. Furthermore, the post-processing algorithm addressed false positives and negatives in the model's predictions. Overall, the proposed approach demonstrated promising results for lung parenchyma segmentation. The method has the potential to be valuable in the advancement of computer-aided diagnosis (CAD) systems for automatic nodule detection.

Boundary-aware registration network for 4D-CT lung image with sliding motion

Background and ObjectiveFast and accurate registration of 4D-CT lung images is significant for respiratory motion modeling and radiotherapy planning. However, since the complex respiratory motion involves sliding motion at lung boundary, the traditional registration methods and regularization terms perform poorly in recovering both sliding and smooth deformation in 4D-CT lung image registration. MethodsIn order to overcome these limitations of the traditional registration methods and regularization terms, we propose a boundary-aware registration model with a spatially adaptive regularization term. We incorporate a lung segmentation network into the registration model. With the lung boundary-aware information from the segmentation network, we construct a spatially adaptive regularization term, which integrates the smooth regularization and the non-smooth regularization, to accommodate both smooth and sliding motion. ResultsWe evaluate the proposed registration model on clinical 4D-CT data and the public DIR-Lab dataset. Our model provides a minimum Target Registration Error (1.59 ± 0.57 mm) of landmarks compared with the other lung registration methods. The ablation studies show that the proposed spatially adaptive regularization term provides superior performance in HD (13.75 ± 3.36 mm) and MHD (1.63 ± 0.32 mm) to the smooth regularization term and non-smooth regularization term. ConclusionsThe proposed boundary-aware registration model enables adaptive regularization term, which can flexibly regulate both the sliding motion at the lung boundary and the smooth motion inside the lung simultaneously. Therefore, our model can perform fast and accurate registration for 4D-CT lung images with sliding motion, which is beneficial to respiratory motion modeling and lung cancer radiotherapy.

The Socialization of Breathing Muscle Stratching Therapy on Increasing Vital Lung Capacity of Theasthma at Grandmed Hospital Lubuk Pakam

Asthma is a disease of shortness of breath due to the narrowing of flight routes. A decrease in the expiratory rate and inspiratory volume lowers the lung fundamentals. Asthmatic patients need exercise to expand the lung boundaries which is very necessary. Respiratory muscle stretching exercises are activities to maintain and build the adaptability or adaptability of the respiratory muscles. This examination aims to determine the impact of prolonged treatment on the respiratory muscles to establish the lung margins that are important for asthmatic patients. This PkM activity is carried out through outreach activities combined with demonstration and discussion methods. The PkM activity begins with the provision of pre-test questions and ends with a post-test to measure the increase in PkM participants' knowledge of the socialized material. This PkM activity has been carried out very well where as many as 17 PkM participants who are nurses at Grandmed Lubuk Pakam Hospital have understood the socialization material presented, namely respiratory muscle stretching therapy to increase the lung vital capacity of asthma patients. After the intervention was given, the lung vital capacity of asthma patients changed to be in the good and very good categories. The knowledge and insights of the PkM participants increased with the demonstrations and discussions given. In addition, there was an increase in the knowledge of PkM participants regarding respiratory muscle stretching therapy in asthma patients with an average percent increase of 20%.

Cross Dataset Analysis of Domain Shift in CXR Lung Region Detection.

Domain shift is one of the key challenges affecting reliability in medical imaging-based machine learning predictions. It is of significant importance to investigate this issue to gain insights into its characteristics toward determining controllable parameters to minimize its impact. In this paper, we report our efforts on studying and analyzing domain shift in lung region detection in chest radiographs. We used five chest X-ray datasets, collected from different sources, which have manual markings of lung boundaries in order to conduct extensive experiments toward this goal. We compared the characteristics of these datasets from three aspects: information obtained from metadata or an image header, image appearance, and features extracted from a pretrained model. We carried out experiments to evaluate and compare model performances within each dataset and across datasets in four scenarios using different combinations of datasets. We proposed a new feature visualization method to provide explanations for the applied object detection network on the obtained quantitative results. We also examined chest X-ray modality-specific initialization, catastrophic forgetting, and model repeatability. We believe the observations and discussions presented in this work could help to shed some light on the importance of the analysis of training data for medical imaging machine learning research, and could provide valuable guidance for domain shift analysis.

Lung parenchyma segmentation based on semantic data augmentation and boundary attention consistency

The paper proposes an automatic, accurate, deep learning segmentation approach applied to the lung parenchyma. In this way, we adopt a set of strategies to produce fine and accurate segmentation. Firstly, we preprocess the image, and fill the regions outside thoracic cavity to improve the contrast of the lung parenchyma. Then, we use semantic data augmentation to get more effective dataset. Specially, we change the regions of original image inside and outside lung parenchyma except for boundary to generate new training images and preserve the invariance of lung boundary. Furthermore, we propose a novel boundary attention consistency to further enhance the attention to lung boundary and promote network to output fine boundary. We choose the UNet network as the baseline model of lung segmentation and apply above strategies to train it. The effectiveness of every component of proposed segmentation method is confirmed by ablation experiments, and the superiority of the proposed segmentation method is fully verified by comparing with other existing segmentation methods.

Computer-Aided Detection of Human Lung Nodules on Computer Tomography Images via Novel Optimized Techniques.

As the mortality rate of lung cancer is enormously high, its impact is also extremely higher than the other types of cancer. Lung malignancy is thus considered one of the deadliest diseases with a high death rate in the world. It is reported that nearly 1.2 million people are diagnosed with this disease and about 1.1 million individuals are died due to this type of cancer every year. The early detection of this disease is the only solution for minimizing the death rate or maximizing the survival rate. However, the timely identification of lung malignant growth is a complex process and hence various imaging algorithms are employed in the process of detecting lung cancer on time. The Computer-Aided Diagnosis (CAD) is highly beneficial for the radiologist to rapidly detect and diagnose the irregularities in advance. The CAD systems usually focus on identifying and detecting the lung nodules. As the treatment of this disease is provided on the basis of its stages, the early detection of cancer has to be given much importance. The major drawbacks of existing CAD systems are less accuracy in segmenting the nodule and staging the lung cancer. The major aim of this work is to categorize the lung nodules from the CT image and classify the tumorous cells for identifying the exact position of cancer with higher sensitivity, precision, and accuracy than other strategies. The methods employed in this study are listed as follows: (i) For the process of de-noising and edge sharpening of lung image, the curvelet transform was used. (ii) The Fuzzy thresholding technique was used to perform lung image binarization and lung boundary corrections. (iii) Segmentation was performed by implementing the K-means algorithm. (iv) By using Convolutional Neural Network (CNN), different stages of lung nodules, like benign and malignant, were identified. The proposed classifier achieves optimal accuracy of 97.3%, a sensitivity of 98.6% and a specificity of 96.1% which are significantly better than the other approaches. Thus, the proposed approach is highly helpful in detecting lung cancer in its early stages. The results validate that the proposed algorithms are highly capable of classifying the lung images into various stages, which effectively helps the radiologist in the decision-making process.

SEGMENTATION AND CLASSIFICATION OF COVID-19 MEDICAL IMAGES USING DEEP NEURAL NETWORK

Corona Virus Disease-2019 (COVID-19), caused by Severe Acute Respiratory Syndrome-Corona Virus-2 (SARS-CoV-2), is a highly contagious disease that has affected the lives of millions around the world. Chest X-Ray (CXR) and Computed Tomography (CT) imaging modalities are widely used to obtain a fast and accurate diagnosis of COVID-19. However, manual identification of the infection through radio images is extremely challenging because it is time-consuming and highly prone to human errors. Artificial Intelligence (AI)-techniques have shown potential and are being exploited further in the development of automated and accurate solutions for COVID-19 detection. In this project, try to establish a new deep convolutional neural network tailored for segmenting the chest CT images with COVID-19 infections as well as the entire lung from chest CT images, referred to as COVID-SegNet. Besides using local binary pattern for feature extraction and its classification using DAENN. Inspired by the observation that the boundary of the infected lung can be enhanced by adjusting the global intensity, in the proposed deep CNN, introduce a feature variation block which adaptively adjusts the global properties of the features for segmenting COVID-19 infection. The proposed FV block can enhance the capability of feature representation effectively and adaptively for diverse cases. We fuse features at different scales by proposing Progressive Atrous Spatial Pyramid Pooling to handle the sophisticated infection areas with diverse appearance and shapes. The proposed method achieves state-of-the-art performance. Dice similarity coefficients are 0.987 and 0.726 for lung and COVID-19 segmentation, respectively. The proposed network enhances the segmentation ability of the COVID-19 infection, makes the connection with other techniques and contributes to the development of remedying COVID-19 infection Keywords—: Coronavirus disease 2019 pneumonia, COVID-19, deep learning, segmentation, multi-scale feature

EfficientUNet: Modified encoder‐decoder architecture for the lung segmentation in chest x‐ray images

AbstractChest x‐rays are a widely used medical imaging technique for the early detection of pulmonary diseases. However, the increasing workload and shortage of radiologists have motivated the research community to explore the possibilities in automated diagnosis. Segmentation is the most critical and essential procedure to improve the automatic diagnosis. Irregular lung shape and size with the overlapped lung regions make the identification of lung boundaries difficult. Further, the dense abnormalities often have high‐intensity values that can be interpreted as lung boundaries. UNet is the most popular segmentation architecture among the deep learning community. Despite its high performance, there is scope for improvement. Most of the proposed segmentation models for lung segmentation in chest x‐rays are trained and tested on a same dataset. It is reported that the model trained on one dataset fails when tested on another dataset. The aim of this study is to develop an efficient architecture for accurate lung segmentation in chest x‐rays. The proposed model uses the pre‐trained encoder, and the decoder uses residual learning and batch normalization to improve the performance. The publicly available Shenzhen, Montgomery County, and the NIH datasets are used for the evaluation of the proposed model. It is also validated by cross‐dataset generalization. The proposed model obtained the 96.14% DSC and 92.67% JI value on the Shenzhen dataset, while 98.48% DSC and 97.01% JI value on the MC dataset. For cross‐dataset evaluation, the EfficientUNet reported the DSC value of 95.97% and JI value of 92.34% for the MC dataset. While the DSC value of 94.02% and JI value of 88.86% on the NIH dataset. The obtained results across the different metrics show the efficiency of the proposed model. Unlike, the other state‐of‐the‐art models, the proposed model is evaluated on the lungs that are highly irregular in shape and size due to different pulmonary abnormalities.

PREFUL MRI Depicts Dual Bronchodilator Changes in COPD: A Retrospective Analysis of a Randomized Controlled Trial.

PurposeTo assess whether dynamic ventilation and perfusion (Q) biomarkers derived by phase-resolved functional lung (PREFUL) MRI can measure treatment response to 14-day therapy with indacaterol-glycopyrronium (IND-GLY) and correlate to clinical outcomes including lung function, symptoms, and cardiac function in patients with chronic obstructive pulmonary disease (COPD), as determined by spirometry, body plethysmography, cardiac MRI, and dyspnea score measurements.Materials and MethodsThe cardiac left ventricular function in COPD (CLAIM) study enrolled patients aged 40 years or older with COPD, stable cardiovascular function, and hyperinflation (residual volume > 135% predicted). Dynamic MRI data of these patients were retrospectively analyzed using the PREFUL technique to assess the effect of 14-day IND-GLY treatment versus placebo on regional measurements of ventilation dynamics. After manual segmentation of the lung parenchyma, flow-volume loops of each voxel were correlated to an individualized reference flow-volume loop, creating a two-dimensional flow-volume loop correlation map (FVL-CM) as a measure of ventilation dynamics. Ventilation-perfusion match (VQM) was evaluated in combination with perfusion and regional ventilation (VQMRVent) and with perfusion and the FVL-CM measurement (VQMCM). For image and statistical analysis, the lung parenchyma was segmented as a region of interest by manually delineating the lung boundary and excluding the large (central) vessels for each section. Differences in ventilation, perfusion, and VQM between IND-GLY and placebo were compared using analysis of variance, with study treatment, patient, and period included as factors.ResultsFifty patients (mean age, 64.3 years ± 7.65 [SD]; 35 men) were included in this analysis. IND-GLY significantly increased mean correlation as measured with FVL-CM versus that of placebo (least squares [LS] means treatment difference: 0.05 [95% CI: 0.03, 0.07]; P < .0001). Compared with placebo, IND-GLY increased mean Q (LS means treatment difference: 9.27 mL/min/100 mL [95% CI: 0.05, 18.49]; P = .049) and improved both VQMCM and VQMRVent (LS means treatment difference: 0.06 [95% CI: 0.03, 0.08]; P < .0001 and 0.05 [95% CI: 0.02, 0.08]; P = .001, respectively).ConclusionRegional ventilation dynamics and VQM measured by PREFUL MRI show treatment response in COPD. Supplemental material is available for this article. Clinical trial registration no. NTR6831Keywords: MRI, COPD, Perfusion, Ventilation, Lung, PulmonaryPublished under a CC BY 4.0 license

H-SegNet: hybrid segmentation network for lung segmentation in chest radiographs using mask region-based convolutional neural network and adaptive closed polyline searching method

Chest x-ray (CXR) is one of the most commonly used imaging techniques for the detection and diagnosis of pulmonary diseases. One critical component in many computer-aided systems, for either detection or diagnosis in digital CXR, is the accurate segmentation of the lung. Due to low-intensity contrast around lung boundary and large inter-subject variance, it has been challenging to segment lung from structural CXR images accurately. In this work, we propose an automatic Hybrid Segmentation Network (H-SegNet) for lung segmentation on CXR. The proposed H-SegNet consists of two key steps: (1) an image preprocessing step based on a deep learning model to automatically extract coarse lung contours; (2) a refinement step to fine-tune the coarse segmentation results based on an improved principal curve-based method coupled with an improved machine learning method. Experimental results on several public datasets show that the proposed method achieves superior segmentation results in lung CXRs, compared with several state-of-the-art methods.

Development of Deep Learning-based Automatic Scan Range Setting Model for Lung Cancer Screening Low-dose CT Imaging

To develop an automatic setting of a deep learning-based system for detecting low-dose computed tomography (CT) lung cancer screening scan range and compare its efficiency with the radiographer's performance. This retrospective study was performed using 1984 lung cancer screening low-dose CT scans obtained between November 2019 and May 2020. Among 1984 CT scans, 600 CT scans were considered suitable for an observational study to explore the relationship between the scout landmarks and the actual lung boundaries. Further, 1144 CT scans data set was used for the development of a deep learning-based algorithm. This data set was split into an 8:2 ratio divided into a training set (80%, n=915) and a validation set (20%, n=229). The performance of the deep learning algorithm was evaluated in the test set (n=240) using actual lung boundaries and radiographers' scan ranges. The mean differences between the upper and lower boundaries of the deep learning-based algorithm and the actual lung boundaries were 4.72 ± 3.15 mm and 16.50 ± 14.06 mm, respectively. The accuracy and over-scanning of the scan ranges generated by the system were 97.08% (233/240) and 0% (0/240) for the upper boundary, and 96.25% (231/240) and 29.58% (71/240) for the lower boundary. The developed deep learning-based algorithm system can effectively predict lung cancer screening low-dose CT scan range with high accuracy using only the frontal scout.

Design Computer-Aided Diagnosis System Based on Chest CT Evaluation of Pulmonary Nodules.

At present, the diagnosis and treatment of lung cancer have always been one of the research hotspots in the medical field. Early diagnosis and treatment of this disease are necessary means to improve the survival rate of lung cancer patients and reduce their mortality. The introduction of computer-aided diagnosis technology can easily, quickly, and accurately identify the lung nodule area as an imaging feature of early lung cancer for the clinical diagnosis of lung cancer and is helpful for the quantitative analysis of the characteristics of lung nodules and is useful for distinguishing benign and malignant lung nodules. Growth provides an objective diagnostic reference standard. This paper studies ITK and VTK toolkits and builds a system platform with MFC. By studying the process of doctors diagnosing lung nodules, the whole system is divided into seven modules: suspected lung shadow detection, image display and image annotation, and interaction. The system passes through the entire lung nodule auxiliary diagnosis process and obtains the number of nodules, the number of malignant nodules, and the number of false positives in each set of lung CT images to analyze the performance of the auxiliary diagnosis system. In this paper, a lung region segmentation method is proposed, which makes use of the obvious differences between the lung parenchyma and other human tissues connected with it, as well as the position relationship and shape characteristics of each human tissue in the image. Experiments are carried out to solve the problems of lung boundary, inaccurate segmentation of lung wall, and depression caused by noise and pleural nodule adhesion. Experiments show that there are 2316 CT images in 8 sets of images of different patients, and the number of nodules is 56. A total of 49 nodules were detected by the system, 7 were missed, and the detection rate was 87.5%. A total of 64 false-positive nodules were detected, with an average of 8 per set of images. This shows that the system is effective for CT images of different devices, pixel pitch, and slice pitch and has high sensitivity, which can provide doctors with good advice.

Relationship between enlarged cardiac silhouette on chest X-ray and left ventricular size on transthoracic echocardiography.

The diagnostic accuracy of the cardiothoracic ratio on chest X-ray to detect left ventricular (LV) enlargement has not been well defined despite its traditional association with cardiomegaly. We aimed to determine whether the cardiothoracic ratio can accurately predict LV enlargement based on indexed linear measurements of the LV on transthoracic echocardiography (TTE). We included consecutive patients who had a TTE and a posteroanterior chest X-ray performed within 90days of each other at a tertiary care center. LV size was determined by measuring the LV end-diastolic dimension (LVEDD) and LV end-diastolic dimension indexed (LVEDDI) to body surface area. The cardiothoracic ratio was calculated by dividing the maximum transverse diameter of the cardiac silhouette by the maximum transverse diameter of the right and left lung boundaries. 173 patients were included in the study (mean age 68 ± 15years, 49.1% female). Mean cardiothoracic ratio was 0.56 ± 0.09, and the mean LVEDD and indexed LVEDDI were of 47 ± 8.6mm and dimension of 27 ± 4.5mm/m2 respectively. There was no significant correlation between the cardiothoracic ratio measured on chest X-ray and either the LVEDD or LVEDDI measured on TTE (r = 0.011, p = 0.879; r = 0.122, p = 0.111). The ability of the cardiothoracic ratio to predict LV enlargement (defined as LVEDDI > 30mm/m2) was not statistically significant. The cardiothoracic ratio on chest X-ray is not a predictor of LV enlargement based on indexed linear measurements of the LV by TTE.

Detection of Pneumonia Using Chest X-Ray Images and Image Processing Algorithms - A Comparative Study

Utilizing exclusively picture handling procedures, this examination proposes an original strategy for distinguishing the presence of pneumonia mists in chest X-rays (CXR). Collected the several analogue chest CXRs from patients with normal and Pneumonia-infected lungs. Indigenous algorithms have been developed for cropping and for extraction of the lung region from the images. To detect pneumonia clouds first conducted the preprocessing of the image then used the image segmentation techniques like Otsu thresholding K-means clustering and global thresholding and then contour detection algorithm was applied which helped to detect lung boundary, the area’s ratio is used to classify the normal lung from pneumonia affected lung.