A Segmentation Framework of Pulmonary Nodules in Lung CT Images.

Accurate segmentation of pulmonary nodules is a prerequisite for acceptable performance of computer-aided detection (CAD) system designed for diagnosis of lung cancer from lung CT images. Accurate segmentation helps to improve the quality of machine level features which could improve the performance of the CAD system. The well-circumscribed solid nodules can be segmented using thresholding, but segmentation becomes difficult for part-solid, non-solid, and solid nodules attached with pleura or vessels. We proposed a segmentation framework for all types of pulmonary nodules based on internal texture (solid/part-solid and non-solid) and external attachment (juxta-pleural and juxta-vascular). In the proposed framework, first pulmonary nodules are categorized into solid/part-solid and non-solid category by analyzing intensity distribution in the core of the nodule. Two separate segmentation methods are developed for solid/part-solid and non-solid nodules, respectively. After determining the category of nodule, the particular algorithm is set to remove attached pleural surface and vessels from the nodule body. The result of segmentation is evaluated in terms of four contour-based metrics and six region-based metrics for 891 pulmonary nodules from Lung Image Database Consortium and Image Database Resource Initiative (LIDC/IDRI) public database. The experimental result shows that the proposed segmentation framework is reliable for segmentation of various types of pulmonary nodules with improved accuracy compared to existing segmentation methods.

An ensemble of three-dimensional deep neural network models for multi-attribute scoring and classification of pulmonary nodules.

Lung cancer is the uncontrolled growth of cells that originates in the lung parenchyma or cells that line the air passages. These cells divide rapidly to form malicious tumors. This paper proposes a multi-task ensemble of three dimensional (3D) deep neural network (DNN) based model, namely: pre-trained EfficientNetB0, BiGRU-based SEResNext101, and the proposed LungNet. The ensemble model performs binary classification and regression tasks to accurately classify the benign and malignant pulmonary nodules. This study also explores the attribute importance and proposes a domain knowledge-based regularization technique. The proposed model is evaluated on the public benchmark LIDC-IDRI dataset. Through a comparative study, it was shown that when coefficients generated by the random forest (RF) are used in the loss function, the proposed ensemble model offers a better prediction capability of the accuracy of 96.4% compared to the state-of-the-art methods. In addition, the receiver operating characteristic curves show that the proposed ensemble model has better performance than the base learners. Thus, the proposed CAD-based model can efficiently detect malignant pulmonary nodules.

Hyperparameter optimization and development of an advanced CNN-based technique for lung nodule assessment.

Objective. This paper aims to propose an advanced methodology for assessing lung nodules using automated techniques with computed tomography (CT) images to detect lung cancer at an early stage.Approach. The proposed methodology utilizes a fixed-size 3 × 3 kernel in a convolution neural network (CNN) for relevant feature extraction. The network architecture comprises 13 layers, including six convolution layers for deep local and global feature extraction. The nodule detection architecture is enhanced by incorporating a transfer learning-based EfficientNetV_2 network (TLEV2N) to improve training performance. The classification of nodules is achieved by integrating the EfficientNet_V2 architecture of CNN for more accurate benign and malignant classification. The network architecture is fine-tuned to extract relevant features using a deep network while maintaining performance through suitable hyperparameters.Main results. The proposed method significantly reduces the false-negative rate, with the network achieving an accuracy of 97.56% and a specificity of 98.4%. Using the 3 × 3 kernel provides valuable insights into minute pixel variation and enables the extraction of information at a broader morphological level. The continuous responsiveness of the network to fine-tune initial values allows for further optimization possibilities, leading to the design of a standardized system capable of assessing diversified thoracic CT datasets.Significance. This paper highlights the potential of non-invasive techniques for the early detection of lung cancer through the analysis of low-dose CT images. The proposed methodology offers improved accuracy in detecting lung nodules and has the potential to enhance the overall performance of early lung cancer detection. By reconfiguring the proposed method, further advancements can be made to optimize outcomes and contribute to developing a standardized system for assessing diverse thoracic CT datasets.

Performance analyses of five neural network classifiers on nodule classification in lung CT images using WEKA: a comparative study.

In this report, we are presenting our work on performance analyses of five different neural network classifiers viz. MLP, DL4JMLP, logistic regression, SGD and simple logistic classifier in lung nodule detection using WEKA interface. To the best of our knowledge, this report demonstrates first use of WEKA for comparative performance analyses of neural network classifiers in identifying lung nodules from lung CT-images. A total of 624 handcrafted features from 52 numbers of lung CT-images collected randomly from Lung Image Database Consortium (LIDC) were fed into WEKA to evaluate the performances of the classifiers under four different categories of computation. Performances of the classifiers were observed in terms of 11 important parameters viz. accuracy, kappa statistic, root mean squared error, TPR, FPR, precision, sensitivity, F-measurement, MCC, ROC area and PRC area. Results show 86.53%, 77.77%, 55.55%, 94.44% & 88.88% accuracy as well as 0.91, 0.86, 0.68, 0.91 & 0.93 ROC area for MLP, DL4JMLP, logistic, SGD and simple logistic classifier respectively at tenfold cross-validation by taking 66% of the data set for training and 34% for testing and validation purpose. SGDClassifier has been found the best performing followed by simple logistic classifier for the purpose.

Accurate classification of lung nodules on CT images using the TransUnet.

Background: Computed tomography (CT) is an effective way to scan for lung cancer. The classification of lung nodules in CT screening is completely doctor dependent, which has drawbacks, including difficulty classifying tiny nodules, subjectivity, and high false-positive rates. In recent years, deep convolutional neural networks, a deep learning technology, have been shown to be effective in medical imaging diagnosis. Herein, we propose a deep convolutional neural network technique (TransUnet) to automatically classify lung nodules accurately. Methods: TransUnet consists of three parts: the transformer, the Unet, and global average pooling (GAP). The transformer encodes discriminative features via global self-attention modeling on CT image patches. The Unet, which collects context by constricting route, enables exact lunge nodule localization. The GAP categorizes CT images, assigning each sample a score. Python was employed to pre-process all CT images in the LIDI-IDRI, and the obtained 8,474 images (3,259 benign and 5,215 lung nodules) were used to evaluate the method's performance. Results: The accuracies of TransUnet in the training and testing sets were 87.90 and 84.62%. The sensitivity, specificity, and AUC of the proposed TransUnet on the testing dataset were 70.92, 93.17, and 0.862%, respectively (0.844-0.879). We also compared TransUnet to three well-known methods, which outperformed these methods. Conclusion: The experimental results on LIDI-IDRI demonstrated that the proposed TransUnet has a great performance in classifying lung nodules and has a great potential application in diagnosing lung cancer.

Changes of resistance rates in Pseudomonas aeruginosa strains are unrelated to antimicrobial consumption in ICU populations with invasive device-related infection.

OBJECTIVE: To evaluate the relationship between antipseudomonal antibiotic consumption and each individual drug resistance rate in Pseudomonas aeruginosa strains causing ICU acquired invasive device-related infections (IDRI). DESIGN: A post hoc analysis was made of the data collected prospectively from the ENVIN-HELICS registry. SETTING: Intensive Care Units participating in the ENVIN-UCI registry between the years 2007 and 2016 (3-month registry each year). PATIENTS: Patients admitted for over 24h. MAIN VARIABLES: Annual linear and nonlinear trends of resistance rates of P. aeruginosa strains identified in IDRI and days of treatment of each antipseudomonal antibiotic family per 1000 occupied ICU bed days (DOT) were calculated. RESULTS: A total of 15,095 episodes of IDRI were diagnosed in 11,652 patients (6.2% out of a total of 187,100). Pseudomonas aeruginosa was identified in 2095 (13.6%) of 15,432 pathogens causing IDRI. Resistance increased significantly over the study period for piperacillin-tazobactam (P<0.001), imipenem (P=0.016), meropenem (P=0.004), ceftazidime (P=0.005) and cefepime (P=0.015), while variations in resistance rates for amikacin, ciprofloxacin, levofloxacin and colistin proved nonsignificant. A significant DOT decrease was observed for aminoglycosides (P<0.001), cephalosporins (P<0.001), quinolones (P<0.001) and carbapenems (P<0.001). CONCLUSIONS: No significant association was observed between consumption of each antipseudomonal antibiotic family and the respective resistance rates for P. aeruginosa strains identified in IDRI.

Machine learning to predict lung nodule biopsy method using CT image features: A pilot study.

Computed tomography (CT)-based screening on lung cancer mortality is poised to make lung nodule management a growing public health problem. Biopsy and pathologic analysis of suspicious nodules is necessary to ensure accurate diagnosis and appropriate intervention. Biopsy techniques vary as do the specialists that perform them and the ways lung nodule patients are referred and triaged. The largest dichotomy is between minimally invasive biopsy (MIB) and surgical biopsy (SB). Cases of unsuccessful MIB preceding a SB can result in considerable delay in definitive care with potentially an adverse impact on prognosis besides potentially avoidable healthcare expenditures. An automated method that predicts the optimal biopsy method for a given lung nodule could save time and healthcare costs by facilitating referral and triage patterns. To our knowledge, no such method has been published. Here, we used CT image features and radiologist-annotated semantic features to predict successful MIB in a way that has not been described before. Using data from the Lung Image Database Consortium image collection (LIDC-IDRI), we trained a logistic regression model to determine whether a MIB or SB procedure was used to diagnose lung cancer in a patient presenting with lung nodules. We found that in successful MIB cases, the nodules were significantly larger and more spiculated. Our model illustrates that using robust machine learning tools on easily accessible semantic and image data can predict whether a patient's nodule is best biopsied by MIB or SB. Pending further validation and optimization, clinicians could use our publicly accessible model to aid clinical decision-making.

Automatic detection of multisize pulmonary nodules in CT images: Large-scale validation of the false-positive reduction step.

PURPOSE: Currently reported computer-aided detection (CAD) approaches face difficulties in identifying the diverse pulmonary nodules in thoracic computed tomography (CT) images, especially in heterogeneous datasets. We present a novel CAD system specifically designed to identify multisize nodule candidates in multiple heterogeneous datasets. METHODS: The proposed CAD scheme is divided into two phases: primary phase and final phase. The primary phase started with the lung segmentation algorithm and the segmented lungs were further refined using morphological closing process to include the pleural nodules. Next, we empirically formulated three subalgorithms modules to detect different sizes of nodule candidates (≥3 and <6 mm; ≥6 and <10 mm; and ≥10 mm). Each subalgorithm module included a multistage flow of rule-based thresholding and morphological processes. In the final phase, the nodule candidates were augmented to boost the performance of the classifier. The CAD system was trained using a total number of nodule candidates = 201,654 (after augmentation) and nonnodule candidates = 731,486. A rich set of 515 features based on cluster, texture, and voxel-based intensity features were utilized to train a neural network classifier. The proposed method was trained on 899 scans from the Lung Image Database Consortium/Image Database Resource Initiative (LIDC-IDRI). The CAD system was also independently tested on 153 CT scans taken from the AAPM-SPIE-LungX Dataset and two subsets from the Early Lung Cancer Action Project (ELCAP and PCF). RESULTS: For the LIDC-IDRI training set, the proposed CAD scheme yielded an overall sensitivity of 85.6% (1189/1390) and 83.5% (1161/1390) at 8 FP/scan and 1 FP/scan, respectively. For the three independent test sets, the CAD system achieved an average sensitivity of 68.4% at 8 FP/scan. CONCLUSION: The authors conclude that the proposed CAD system can identify dissimilar nodule candidates in the multiple heterogeneous datasets. It could be considered as a useful tool to support radiologists during screening trials.

Measuring Interobserver Disagreement in Rating Diagnostic Characteristics of Pulmonary Nodule Using the Lung Imaging Database Consortium and Image Database Resource Initiative.

RATIONALE AND OBJECTIVES: The purpose of this study was to measure and analyze interobserver disagreement in rating diagnostic characteristics of pulmonary nodules on computed tomography scans using the Lung Imaging Database Consortium and Image Database Resource Initiative (LIDC/IDRI) database, and then to provide investigators with understanding the variability in rating diagnostic characteristics among radiologists. MATERIALS AND METHODS: A histogram-based accumulated nodule-level approach is proposed to measure interobserver disagreement in rating diagnostic characteristics of pulmonary nodules among radiologists. The mean rating differences of radiologists on nodule level are calculated; next, a histogram of the accumulated nodule-level disagreements is constructed; and finally, mean, variance, skewness, and kurtosis statistics based on the histogram are extracted to analyze and summary interobserver disagreement in terms of the assessment of diagnostic characteristics of radiologists. Using the developed computer scheme, the disagreement of radiologists in rating all of 1880 distinct nodules from 1018 computed tomography scans are analyzed using original ratings as well as combined ratings according to the LIDC/IDRI instruction. RESULTS: The interobserver disagreement in rating diagnostic characteristics according to the defined categories of the LIDC/IDRI is substantial. The mean values of disagreement range from 0.0052 to 0.2341. The highest disagreement lies in rating subtlety characteristics, whereas internal structure receives the lowest disagreement of 0.0052. The calcification, texture, spiculation, lobulation, malignancy, sphericity, and margin receive disagreements of 0.0393, 0.1351, 0.1616, 0.1943, 0.2144, 0.2174, and 0.2228, respectively. CONCLUSIONS: Disagreements exist across radiologists in rating diagnostic characteristics of pulmonary nodules, and the disagreement levels vary from each other. Agreement among radiologists is improved by combining ratings according to the LIDC/IDRI instruction. For investigators, understanding and appreciating the disagreement level of each diagnostic characteristic is required when using them in related researches.

Segmentation of pulmonary nodules in computed tomography using a regression neural network approach and its application to the Lung Image Database Consortium and Image Database Resource Initiative dataset.

We present new pulmonary nodule segmentation algorithms for computed tomography (CT). These include a fully-automated (FA) system, a semi-automated (SA) system, and a hybrid system. Like most traditional systems, the new FA system requires only a single user-supplied cue point. On the other hand, the SA system represents a new algorithm class requiring 8 user-supplied control points. This does increase the burden on the user, but we show that the resulting system is highly robust and can handle a variety of challenging cases. The proposed hybrid system starts with the FA system. If improved segmentation results are needed, the SA system is then deployed. The FA segmentation engine has 2 free parameters, and the SA system has 3. These parameters are adaptively determined for each nodule in a search process guided by a regression neural network (RNN). The RNN uses a number of features computed for each candidate segmentation. We train and test our systems using the new Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) data. To the best of our knowledge, this is one of the first nodule-specific performance benchmarks using the new LIDC-IDRI dataset. We also compare the performance of the proposed methods with several previously reported results on the same data used by those other methods. Our results suggest that the proposed FA system improves upon the state-of-the-art, and the SA system offers a considerable boost over the FA system.

Development of a personalized training system using the Lung Image Database Consortium and Image Database resource Initiative Database.

RATIONALE AND OBJECTIVES: The aim of this study was to develop a personalized training system using the Lung Image Database Consortium (LIDC) and Image Database resource Initiative (IDRI) Database, because collecting, annotating, and marking a large number of appropriate computed tomography (CT) scans, and providing the capability of dynamically selecting suitable training cases based on the performance levels of trainees and the characteristics of cases are critical for developing a efficient training system. MATERIALS AND METHODS: A novel approach is proposed to develop a personalized radiology training system for the interpretation of lung nodules in CT scans using the Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI) database, which provides a Content-Boosted Collaborative Filtering (CBCF) algorithm for predicting the difficulty level of each case of each trainee when selecting suitable cases to meet individual needs, and a diagnostic simulation tool to enable trainees to analyze and diagnose lung nodules with the help of an image processing tool and a nodule retrieval tool. RESULTS: Preliminary evaluation of the system shows that developing a personalized training system for interpretation of lung nodules is needed and useful to enhance the professional skills of trainees. CONCLUSIONS: The approach of developing personalized training systems using the LIDC/IDRL database is a feasible solution to the challenges of constructing specific training program in terms of cost and training efficiency.

New vaccines for neglected parasitic diseases and dengue.

Neglected tropical diseases (NTDs) are a significant source of morbidity and socioeconomic burden among the world's poor. Virtually all of the 2.4 billion people who live on less than $2 per d, more than a third of the world's population, are at risk for these debilitating NTDs. Although chemotherapeutic measures exist for many of these pathogens, they are not sustainable countermeasures on their own because of rates of reinfection, risk of drug resistance, and inconsistent maintenance of drug treatment programs. Preventative and therapeutic NTD vaccines are needed as long-term solutions. Because there is no market in the for-profit sector of vaccine development for these pathogens, much of the effort to develop vaccines is driven by nonprofit entities, mostly through product development partnerships. This review describes the progress of vaccines under development for many of the NTDs, with a specific focus on those about to enter or that are currently in human clinical trials. Specifically, we report on the progress on dengue, hookworm, leishmaniasis, schistosomiasis, Chagas disease, and onchocerciasis vaccines. These products will be some of the first with specific objectives to aid the world's poorest populations.