Lung nodule classification with multilevel patch-based context analysis

An anthropomorphic diagnosis system of pulmonary nodules using weak annotation-based deep learning

The accurate categorization of lung nodules in CT scans is an essential aspect in the prompt detection and diagnosis of lung cancer. The categorization of grade and texture for nodules is particularly significant since it can aid radiologists and clinicians to make better-informed decisions concerning the management of nodules. However, currently existing nodule classification techniques have a singular function of nodule classification and rely on an extensive amount of high-quality annotation data, which does not meet the requirements of clinical practice. To address this issue, we develop an anthropomorphic diagnosis system of pulmonary nodules (PN) based on deep learning (DL) that is trained by weak annotation data and has comparable performance to full-annotation based diagnosis systems. The proposed system uses DL models to classify PNs (benign vs. malignant) with weak annotations, which eliminates the need for time-consuming and labor-intensive manual annotations of PNs. Moreover, the PN classification networks, augmented with handcrafted shape features acquired through the ball-scale transform technique, demonstrate capability to differentiate PNs with diverse labels, including pure ground-glass opacities, part-solid nodules, and solid nodules. Through 5-fold cross-validation on two datasets, the system achieved the following results: (1) an Area Under Curve (AUC) of 0.938 for PN localization and an AUC of 0.912 for PN differential diagnosis on the LIDC-IDRI dataset of 814 testing cases, (2) an AUC of 0.943 for PN localization and an AUC of 0.815 for PN differential diagnosis on the in-house dataset of 822 testing cases. In summary, our system demonstrates efficient localization and differential diagnosis of PNs in a resource limited environment, and thus could be translated into clinical use in the future.

GC-WIR : 3D global coordinate attention wide inverted ResNet network for pulmonary nodules classification

PURPOSE

Currently, deep learning methods for the classification of benign and malignant lung nodules encounter challenges encompassing intricate and unstable algorithmic models, limited data adaptability, and an abundance of model parameters.To tackle these concerns, this investigation introduces a novel approach: the 3D Global Coordinated Attention Wide Inverted ResNet Network (GC-WIR). This network aims to achieve precise classification of benign and malignant pulmonary nodules, leveraging its merits of heightened efficiency, parsimonious parameterization, and robust stability.

METHODS

Within this framework, a 3D Global Coordinate Attention Mechanism (3D GCA) is designed to compute the features of the input images by converting 3D channel information and multi-dimensional positional cues. By encompassing both global channel details and spatial positional cues, this approach maintains a judicious balance between flexibility and computational efficiency. Furthermore, the GC-WIR architecture incorporates a 3D Wide Inverted Residual Network (3D WIRN), which augments feature computation by expanding input channels. This augmentation mitigates information loss during feature extraction, expedites model convergence, and concurrently enhances performance. The utilization of the inverted residual structure imbues the model with heightened stability.

RESULTS

Empirical validation of the GC-WIR method is performed on the LUNA 16 dataset, yielding predictions that surpass those generated by previous models. This novel approach achieves an impressive accuracy rate of 94.32%, coupled with a specificity of 93.69%. Notably, the model's parameter count remains modest at 5.76M, affording optimal classification accuracy.

CONCLUSION

Furthermore, experimental results unequivocally demonstrate that, even under stringent computational constraints, GC-WIR outperforms alternative deep learning methodologies, establishing a new benchmark in performance.

ADGAN: Attribute-Driven Generative Adversarial Network for Synthesis and Multiclass Classification of Pulmonary Nodules

Lung cancer is the leading cause of cancer-related deaths worldwide. According to the American Cancer Society, early diagnosis of pulmonary nodules in computed tomography (CT) scans can improve the five-year survival rate up to 70% with proper treatment planning. In this article, we propose an attribute-driven Generative Adversarial Network (ADGAN) for synthesis and multiclass classification of Pulmonary Nodules. A self-attention U-Net (SaUN) architecture is proposed to improve the generation mechanism of the network. The generator is designed with two modules, namely, self-attention attribute module (SaAM) and a self-attention spatial module (SaSM). SaAM generates a nodule image based on given attributes whereas SaSM specifies the nodule region of the input image to be altered. A reconstruction loss along with an attention localization loss (AL) is used to produce an attention map prioritizing the nodule regions. To avoid resemblance between a generated image and a real image, we further introduce an adversarial loss containing a regularization term based on KL divergence. The discriminator part of the proposed model is designed to achieve the multiclass nodule classification task. Our proposed approach is validated over two challenging publicly available datasets, namely LIDC-IDRI and LUNGX. Exhaustive experimentation on these two datasets clearly indicate that we have achieved promising classification accuracy as compared to other state-of-the-art methods.

Pulmonary Nodule Classification Using a Multiview Residual Selective Kernel Network

Lung cancer is one of the leading causes of death worldwide and early detection is crucial to reduce the mortality. A reliable computer-aided diagnosis (CAD) system can help facilitate early detection of malignant nodules. Although existing methods provide adequate classification accuracy, there is still room for further improvement. This study is dedicated to investigating a new CAD scheme for predicting the malignant likelihood of lung nodules in computed tomography (CT) images in light of a deep learning strategy. Conceived from the residual learning and selective kernel, we investigated an efficient residual selective kernel (RSK) block to handle the diversity of lung nodules with various shapes and obscure structures. Founded on this RSK block, we established a multiview RSK network (MRSKNet), to which three anatomical planes in the axial, coronal, and sagittal directions were fed. To reinforce the classification efficiency, seven handcrafted texture features with a filter-like computation strategy were explored, among which the homogeneity (HOM) feature maps are combined with the corresponding intensity CT images for concatenation input, leading to an improved network architecture. Evaluated on the public benchmark Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) challenge database with ten-fold cross validation of binary classification, our experimental results indicated high area under receiver operating characteristic (AUC) and accuracy scores. A better compromise between recall and specificity was struck using the suggested concatenation strategy comparing to many state-of-the-art approaches. The proposed pulmonary nodule classification framework exhibited great efficacy and achieved a higher AUC of 0.9711. The association of handcrafted texture features with deep learning models is promising in advancing the classification performance. The developed pulmonary nodule CAD network architecture is of potential in facilitating the diagnosis of lung cancer for further image processing applications.

A novel fusion algorithm for benign-malignant lung nodule classification on CT images

The accurate recognition of malignant lung nodules on CT images is critical in lung cancer screening, which can offer patients the best chance of cure and significant reductions in mortality from lung cancer. Convolutional Neural Network (CNN) has been proven as a powerful method in medical image analysis. Radiomics which is believed to be of interest based on expert opinion can describe high-throughput extraction from CT images. Graph Convolutional Network explores the global context and makes the inference on both graph node features and relational structures. In this paper, we propose a novel fusion algorithm, RGD, for benign-malignant lung nodule classification by incorporating Radiomics study and Graph learning into the multiple Deep CNNs to form a more complete and distinctive feature representation, and ensemble the predictions for robust decision-making. The proposed method was conducted on the publicly available LIDC-IDRI dataset in a 10-fold cross-validation experiment and it obtained an average accuracy of 93.25%, a sensitivity of 89.22%, a specificity of 95.82%, precision of 92.46%, F1 Score of 0.9114 and AUC of 0.9629. Experimental results illustrate that the RGD model achieves superior performance compared with the state-of-the-art methods. Moreover, the effectiveness of the fusion strategy has been confirmed by extensive ablation studies. In the future, the proposed model which performs well on the pulmonary nodule classification on CT images will be applied to increase confidence in the clinical diagnosis of lung cancer.

Multi-Modal Feature Fusion-Based Multi-Branch Classification Network for Pulmonary Nodule Malignancy Suspiciousness Diagnosis

Detecting and identifying malignant nodules on chest computed tomography (CT) plays an important role in the early diagnosis and timely treatment of lung cancer, which can greatly reduce the number of deaths worldwide. In view of the existing methods in pulmonary nodule diagnosis, the importance of clinical radiological structured data (laboratory examination, radiological data) is ignored for the accuracy judgment of patients' condition. Hence, a multi-modal fusion multi-branch classification network is constructed to detect and classify pulmonary nodules in this work: (1) Radiological data of pulmonary nodules are used to construct structured features of length 9. (2) A multi-branch fusion-based effective attention mechanism network is designed for 3D CT Patch unstructured data, which uses 3D ECA-ResNet to dynamically adjust the extracted features. In addition, feature maps with different receptive fields from multi-layer are fully fused to obtain representative multi-scale unstructured features. (3) Multi-modal feature fusion of structured data and unstructured data is performed to distinguish benign and malignant nodules. Numerous experimental results show that this advanced network can effectively classify the benign and malignant pulmonary nodules for clinical diagnosis, which achieves the highest accuracy (94.89%), sensitivity (94.91%), and F1-score (94.65%) and lowest false positive rate (5.55%).

Two-Stage Selective Ensemble of CNN via Deep Tree Training for Medical Image Classification

Medical image classification is an important task in computer-aided diagnosis systems. Its performance is critically determined by the descriptiveness and discriminative power of features extracted from images. With rapid development of deep learning, deep convolutional neural networks (CNNs) have been widely used to learn the optimal high-level features from the raw pixels of images for a given classification task. However, due to the limited amount of labeled medical images with certain quality distortions, such techniques crucially suffer from the training difficulties, including overfitting, local optimums, and vanishing gradients. To solve these problems, in this article, we propose a two-stage selective ensemble of CNN branches via a novel training strategy called deep tree training (DTT). In our approach, DTT is adopted to jointly train a series of networks constructed from the hidden layers of CNN in a hierarchical manner, leading to the advantage that vanishing gradients can be mitigated by supplementing gradients for hidden layers of CNN, and intrinsically obtain the base classifiers on the middle-level features with minimum computation burden for an ensemble solution. Moreover, the CNN branches as base learners are combined into the optimal classifier via the proposed two-stage selective ensemble approach based on both accuracy and diversity criteria. Extensive experiments on CIFAR-10 benchmark and two specific medical image datasets illustrate that our approach achieves better performance in terms of accuracy, sensitivity, specificity, and F1 score measurement.

Lung Nodule Segmentation and Recognition Algorithm Based on Multiposition U-Net

Lung nodules are the main lesions of the lung, and conditions of the lung can be directly displayed through CT images. Due to the limited pixel number of lung nodules in the lung, doctors have the risk of missed detection and false detection in the detection process. In order to reduce doctors' work intensity and assist doctors to make accurate diagnosis, a lung nodule segmentation and recognition algorithm is proposed by simulating doctors' diagnosis process with computer intelligent methods. Firstly, the attention mechanism model is established to focus on the region of lung parenchyma. Then, a pyramid network of bidirectional enhancement features is established from multiple body positions to extract lung nodules. Finally, the morphological and imaging features of lung nodules are calculated, and then, the signs of lung nodules can be identified. The experiments show that the algorithm conforms to the doctor's diagnosis process, focuses the region of interest step by step, and achieves good results in lung nodule segmentation and recognition.

Diagnosis of Esophageal Lesions by Multi-Classification and Segmentation Using an Improved Multi-Task Deep Learning Model

It is challenging for endoscopists to accurately detect esophageal lesions during gastrointestinal endoscopic screening due to visual similarities among different lesions in terms of shape, size, and texture among patients. Additionally, endoscopists are busy fighting esophageal lesions every day, hence the need to develop a computer-aided diagnostic tool to classify and segment the lesions at endoscopic images to reduce their burden. Therefore, we propose a multi-task classification and segmentation (MTCS) model, including the Esophageal Lesions Classification Network (ELCNet) and Esophageal Lesions Segmentation Network (ELSNet). The ELCNet was used to classify types of esophageal lesions, and the ELSNet was used to identify lesion regions. We created a dataset by collecting 805 esophageal images from 255 patients and 198 images from 64 patients to train and evaluate the MTCS model. Compared with other methods, the proposed not only achieved a high accuracy (93.43%) in classification but achieved a dice similarity coefficient (77.84%) in segmentation. In conclusion, the MTCS model can boost the performance of endoscopists in the detection of esophageal lesions as it can accurately multi-classify and segment the lesions and is a potential assistant for endoscopists to reduce the risk of oversight.

An Intelligent Radiomic Approach for Lung Cancer Screening

The efficiency of lung cancer screening for reducing mortality is hindered by the high rate of false positives. Artificial intelligence applied to radiomics could help to early discard benign cases from the analysis of CT scans. The available amount of data and the fact that benign cases are a minority, constitutes a main challenge for the successful use of state of the art methods (like deep learning), which can be biased, over-fitted and lack of clinical reproducibility. We present an hybrid approach combining the potential of radiomic features to characterize nodules in CT scans and the generalization of the feed forward networks. In order to obtain maximal reproducibility with minimal training data, we propose an embedding of nodules based on the statistical significance of radiomic features for malignancy detection. This representation space of lesions is the input to a feed forward network, which architecture and hyperparameters are optimized using own-defined metrics of the diagnostic power of the whole system. Results of the best model on an independent set of patients achieve 100% of sensitivity and 83% of specificity (AUC = 0.94) for malignancy detection.

Texture Analysis and its Applications in Biomedical Imaging: A Survey

Texture analysis describes a variety of image analysis techniques that quantify the variation in intensity and pattern. This paper provides an overview of several texture analysis approaches addressing the rationale supporting them, their advantages, drawbacks, and applications. This surveys emphasis is in collecting and categorising over five decades of active research on texture analysis. Brief descriptions of different approaches are presented along with application examples. From a broad range of texture analysis applications, this surveys final focus is on biomedical image analysis. An up-to-date list of biological tissues and organs in which disorders produce texture changes that may be used to spot disease onset and progression is provided. Finally, the role of texture analysis methods as biomarkers of disease is summarised.

False positive reduction in pulmonary nodule classification using 3D texture and edge feature in CT images

BACKGROUND

Pulmonary nodule detection can significantly influence the early diagnosis of lung cancer while is confused by false positives.

OBJECTIVE

In this study, we focus on the false positive reduction and present a method for accurate and rapid detection of pulmonary nodule from suspective regions with 3D texture and edge feature.

METHODS

This work mainly consists of four modules. Firstly, small pulmonary nodule candidates are preprocessed by a reconstruction approach for enhancing 3D image feature. Secondly, a texture feature descriptor is proposed, named cross-scale local binary patterns (CS-LBP), to extract spatial texture information. Thirdly, we design a 3D edge feature descriptor named orthogonal edge orientation histogram (ORT-EOH) to obtain spatial edge information. Finally, hierarchical support vector machines (H-SVMs) is used to classify suspective regions as either nodules or non-nodules with joint CS-LBP and ORT-EOH feature vector.

RESULTS

For the solitary solid nodule, ground-glass opacity, juxta-vascular nodule and juxta-pleural nodule, average sensitivity, average specificity and average accuracy of our method are 95.69%, 96.95% and 96.04%, respectively. The elapsed time in training and testing stage are 321.76 s and 5.69 s.

CONCLUSIONS

Our proposed method has the best performance compared with other state-of-the-art methods and is shown the improved precision of pulmonary nodule detection with computationaly low cost.

Deep Learning for the Classification of Small (≤2 cm) Pulmonary Nodules on CT Imaging: A Preliminary Study

RATIONALE AND OBJECTIVES

We aimed to present a deep learning-based malignancy prediction model (CT-lungNET) that is simpler and faster to use in the diagnosis of small (≤2 cm) pulmonary nodules on nonenhanced chest CT and to preliminarily evaluate its performance and usefulness for human reviewers.

MATERIALS AND METHODS

A total of 173 whole nonenhanced chest CT images containing 208 pulmonary nodules (94 malignant and 11 benign nodules) ranging in size from 5 mm to 20 mm were collected. Pathologically confirmed nodules or nodules that remained unchanged for more than 1 year were included, and 30 benign and 30 malignant nodules were randomly assigned into the test set. We designed CT-lungNET to include three convolutional layers followed by two fully-connected layers and compared its diagnostic performance and processing time with those of AlexNET by using the area under the receiver operating curve (AUROC). An observer performance test was conducted involving eight human reviewers of four different groups (medical students, physicians, radiologic residents, and thoracic radiologists) at test 1 and test 2, referring to the CT-lungNET's malignancy prediction rate with pairwise comparison receiver operating curve analysis.

RESULTS

CT-lungNET showed an improved AUROC (0.85; 95% confidence interval: 0.74-0.93), compared to that of the AlexNET (0.82; 95% confidence interval: 0.71-0.91). The processing speed per one image slice for CT-lungNET was about 10 times faster than that for AlexNET (0.90 vs. 8.79 seconds). During the observer performance test, the classification performance of nonradiologists was increased with the aid of CTlungNET, (mean AUC improvement: 0.13; range: 0.03-0.19) but not significantly so in the radiologists group (mean AUC improvement: 0.02; range: -0.02 to 0.07).

CONCLUSION

CT-lungNET was able to provide better classification results with a significantly shorter amount of processing time as compared to AlexNET in the diagnosis of small pulmonary nodules on nonenhanced chest CT. In this preliminary observer performance test, CT-lungNET may have a role acting as a second reviewer for less experienced reviewers, resulting in enhanced performance in the diagnosis of early lung cancer.

An introduction to deep learning in medical physics: advantages, potential, and challenges

As one of the most popular approaches in artificial intelligence, deep learning (DL) has attracted a lot of attention in the medical physics field over the past few years. The goals of this topical review article are twofold. First, we will provide an overview of the method to medical physics researchers interested in DL to help them start the endeavor. Second, we will give in-depth discussions on the DL technology to make researchers aware of its potential challenges and possible solutions. As such, we divide the article into two major parts. The first part introduces general concepts and principles of DL and summarizes major research resources, such as computational tools and databases. The second part discusses challenges faced by DL, present available methods to mitigate some of these challenges, as well as our recommendations.

A manifold learning regularization approach to enhance 3D CT image-based lung nodule classification

PURPOSE

Diagnosis of lung cancer requires radiologists to review every lung nodule in CT images. Such a process can be very time-consuming, and the accuracy is affected by many factors, such as experience of radiologists and available diagnosis time. To address this problem, we proposed to develop a deep learning-based system to automatically classify benign and malignant lung nodules.

METHODS

The proposed method automatically determines benignity or malignancy given the 3D CT image patch of a lung nodule to assist diagnosis process. Motivated by the fact that real structure among data is often embedded on a low-dimensional manifold, we developed a novel manifold regularized classification deep neural network (MRC-DNN) to perform classification directly based on the manifold representation of lung nodule images. The concise manifold representation revealing important data structure is expected to benefit the classification, while the manifold regularization enforces strong, but natural constraints on network training, preventing over-fitting.

RESULTS

The proposed method achieves accurate manifold learning with reconstruction error of ~ 30 HU on real lung nodule CT image data. In addition, the classification accuracy on testing data is 0.90 with sensitivity of 0.81 and specificity of 0.95, which outperforms state-of-the-art deep learning methods.

CONCLUSION

The proposed MRC-DNN facilitates an accurate manifold learning approach for lung nodule classification based on 3D CT images. More importantly, MRC-DNN suggests a new and effective idea of enforcing regularization for network training, possessing the potential impact to a board range of applications.

An Appraisal of Lung Nodules Automatic Classification Algorithms for CT Images

Lung cancer is one of the most deadly diseases around the world representing about 26% of all cancers in 2017. The five-year cure rate is only 18% despite great progress in recent diagnosis and treatment. Before diagnosis, lung nodule classification is a key step, especially since automatic classification can help clinicians by providing a valuable opinion. Modern computer vision and machine learning technologies allow very fast and reliable CT image classification. This research area has become very hot for its high efficiency and labor saving. The paper aims to draw a systematic review of the state of the art of automatic classification of lung nodules. This research paper covers published works selected from the Web of Science, IEEEXplore, and DBLP databases up to June 2018. Each paper is critically reviewed based on objective, methodology, research dataset, and performance evaluation. Mainstream algorithms are conveyed and generic structures are summarized. Our work reveals that lung nodule classification based on deep learning becomes dominant for its excellent performance. It is concluded that the consistency of the research objective and integration of data deserves more attention. Moreover, collaborative works among developers, clinicians, and other parties should be strengthened.

An Automatic Classification Method on Chronic Venous Insufficiency Images

Chronic venous insufficiency (CVI) affect a large population, and it cannot heal without doctors' interventions. However, many patients do not get the medical advisory service in time. At the same time, the doctors also need an assistant tool to classify the patients according to the severity level of CVI. We propose an automatic classification method, named CVI-classifier to help doctors and patients. In this approach, first, low-level image features are mapped into middle-level semantic features by a concept classifier, and a multi-scale semantic model is constructed to form the image representation with rich semantics. Second, a scene classifier is trained using an optimized feature subset calculated by the high-order dependency based feature selection approach, and is used to estimate CVI's severity. At last, classification accuracy, kappa coefficient, F1-score are used to evaluate classification performance. Experiments on the CVI images from 217 patients' medical records demonstrated superior performance and efficiency for CVI-classifier, with classification accuracy up to 90.92%, kappa coefficient of 0.8735 and F1score of 0.9006. This method also outperformed doctors' diagnosis (doctors rely solely on images to make judgments) with accuracy, kappa and F1-score improved by 9.11%, 0.1250 and 0.0955 respectively.

Characterization of Pulmonary Nodules Based on Features of Margin Sharpness and Texture

Lung cancer is the leading cause of cancer-related deaths in the world, and one of its manifestations occurs with the appearance of pulmonary nodules. The classification of pulmonary nodules may be a complex task to specialists due to temporal, subjective, and qualitative aspects. Therefore, it is important to integrate computational tools to the early pulmonary nodule classification process, since they have the potential to characterize objectively and quantitatively the lesions. In this context, the goal of this work is to perform the classification of pulmonary nodules based on image features of texture and margin sharpness. Computed tomography scans were obtained from a publicly available image database. Texture attributes were extracted from a co-occurrence matrix obtained from the nodule volume. Margin sharpness attributes were extracted from perpendicular lines drawn over the borders on all nodule slices. Feature selection was performed by different algorithms. Classification was performed by several machine learning classifiers and assessed by the area under the receiver operating characteristic curve, sensitivity, specificity, and accuracy. Highest classification performance was obtained by a random forest algorithm with all 48 extracted features. However, a decision tree using only two selected features obtained statistically equivalent performance on sensitivity and specificity.

A novel pixel value space statistics map of the pulmonary nodule for classification in computerized tomography images

Accurate assessment of pulmonary nodules can help to diagnose the serious degree of lung cancer. In most computed aided diagnosis (CADx) systems, the feature extraction module plays quite an important role in classifying pulmonary nodules based on different attributes of them. To precisely evaluate the malignancy of an unknown pulmonary nodule, this paper first proposes a novel pixel value space statistics map (PVSSM) for pulmonary nodules classification. By means of PVSSM this study can transform an original two-dimensional (2D) or three-dimensional (3D) pulmonary nodule into a 2D feature matrix, which contributes to better classifying a pulmonary nodule. To validate the proposed method, this study assembled 5385 valid 3D nodules from 1006 cases in LIDC-IDRI database. This study extracts sets of features from the created feature matrixes by singular value decomposition (SVD) method. Using several popular classifiers including KNN, random forest and SVM, we acquire the classification accuracies of 77.29%, 80.07% and 84.21%, respectively. Moreover, this study also utilizes the convolutional neural network (CNN) to assess the malignancy of nodules and the sensitivity, specificity and area under the curve (AUC) reach up to 86.0%, 88.5% and 0.913, respectively. Experiments demonstrate that the PVSSM has a benefit for nodules classification.

Risk Analysis for Pathological Changes in Pulmonary Parenchyma Based on Lung Computed Tomography Images

OBJECTIVE

The purpose of this study is to design a content-based medical image retrieval system, which helps excavate and assess pathological change of pulmonary parenchyma for risks analysis.

METHODS

A data set including lung computed tomography images obtained from 115 patients who experienced pathological changes in pulmonary parenchyma is used. Using morphological theory, images are preprocessed and decomposed into groups of pixel blocks (words), which construct vocabulary. A latent Dirichlet allocation (LDA) model is constructed to assess each image for risk analysis with the method of leave-one-out cross-validation. The precision and recall rate are used as the performance assessment criteria.

RESULTS

The LDA model generates a relevance rank of retrieval results from high to low. From the top 50 images, precision of identical tissue is 0.76 ± 0.031 and precision of each attribute of pulmonary parenchyma range from 0.776 ± 0.043 to 0.984 ± 0.008.

CONCLUSIONS

The study results demonstrate that the proposed LDA model is conductive to lung computed tomography image retrieval and has reliable efficacy on risk analysis about pathological changes of pulmonary parenchyma.

An Assisted Diagnosis System for Detection of Early Pulmonary Nodule in Computed Tomography Images

Lung cancer is still the most concerned disease around the world. Lung nodule generates in the pulmonary parenchyma which indicates the latent risk of lung cancer. Computer-aided pulmonary nodules detection system is necessary, which can reduce diagnosis time and decrease mortality of patients. In this study, we have proposed a new computer aided diagnosis (CAD) system for detection of early pulmonary nodule, which can help radiologists quickly locate suspected nodules and make judgments. This system consists of four main sections: pulmonary parenchyma segmentation, nodule candidate detection, features extraction (total 22 features) and nodule classification. The publicly available data set created by the Lung Image Database Consortium (LIDC) is used for training and testing. This study selects 6400 slices from 80 CT scans containing totally 978 nodules, which is labeled by four radiologists. Through a fast segmentation method proposed in this paper, pulmonary nodules including 888 true nodules and 11,379 false positive nodules are segmented. By means of an ensemble classifier, Random Forest (RF), this study acquires 93.2, 92.4, 94.8, 97.6% of accuracy, sensitivity, specificity, area under the curve (AUC), respectively. Compared with support vector machine (SVM) classifier, RF can reduce more false positive nodules and acquire larger AUC. With the help of this CAD system, radiologist can be provided with a great reference for pulmonary nodule diagnosis timely.

Pulmonary Nodule Classification with Deep Convolutional Neural Networks on Computed Tomography Images

Computer aided detection (CAD) systems can assist radiologists by offering a second opinion on early diagnosis of lung cancer. Classification and feature representation play critical roles in false-positive reduction (FPR) in lung nodule CAD. We design a deep convolutional neural networks method for nodule classification, which has an advantage of autolearning representation and strong generalization ability. A specified network structure for nodule images is proposed to solve the recognition of three types of nodules, that is, solid, semisolid, and ground glass opacity (GGO). Deep convolutional neural networks are trained by 62,492 regions-of-interest (ROIs) samples including 40,772 nodules and 21,720 nonnodules from the Lung Image Database Consortium (LIDC) database. Experimental results demonstrate the effectiveness of the proposed method in terms of sensitivity and overall accuracy and that it consistently outperforms the competing methods.

Comparative evaluation of newly developed model-based and commercially available hybrid-type iterative reconstruction methods and filter back projection method in terms of accuracy of computer-aided volumetry (CADv) for low-dose CT protocols in phantom study

PURPOSE

To directly compare the capability of three reconstruction methods using, respectively, forward projected model-based iterative reconstruction (FIRST), adaptive iterative dose reduction using three dimensional processing (AIDR 3D) and filter back projection (FBP) for radiation dose reduction and accuracy of computer-aided volumetry (CADv) measurements on chest CT examination in a phantom study.

MATERIALS AND METHODS

An anthropomorphic thoracic phantom with 30 simulated nodules of three density types (100, -630, and -800 HU) and five different diameters was scanned with an area-detector CT at tube currents of 270, 200, 120, 80, 40, 20, and 10mA. Each scanned data set was reconstructed as thin-section CT with three methods, and all simulated nodules were measured with CADv software. For comparison of the capability for CADv at each tube current, Tukey's HSD test was used to compare the percentage of absolute measurement errors for all three reconstruction methods. Absolute percentage measurement errors were then compared by means of Dunett's test for each tube current at 270mA (standard tube current).

RESULTS

Mean absolute measurement errors of AIDR 3D and FIRST methods for each nodule type were significantly lower than those of the FBP method at 20mA and 10mA (p<0.05). In addition, absolute measurement errors of the FBP method at 20mA and 10mA was significantly higher than that at 270mA for all nodule types (p<0.05).

CONCLUSION

The FIRST and AIDR 3D methods are more effective than the FBP method for radiation dose reduction, while yielding better measurement accuracy of CADv for chest CT examination.

Dictionary Pruning with Visual Word Significance for Medical Image Retrieval

Content-based medical image retrieval (CBMIR) is an active research area for disease diagnosis and treatment but it can be problematic given the small visual variations between anatomical structures. We propose a retrieval method based on a bag-of-visual-words (BoVW) to identify discriminative characteristics between different medical images with Pruned Dictionary based on Latent Semantic Topic description. We refer to this as the PD-LST retrieval. Our method has two main components. First, we calculate a topic-word significance value for each visual word given a certain latent topic to evaluate how the word is connected to this latent topic. The latent topics are learnt, based on the relationship between the images and words, and are employed to bridge the gap between low-level visual features and high-level semantics. These latent topics describe the images and words semantically and can thus facilitate more meaningful comparisons between the words. Second, we compute an overall-word significance value to evaluate the significance of a visual word within the entire dictionary. We designed an iterative ranking method to measure overall-word significance by considering the relationship between all latent topics and words. The words with higher values are considered meaningful with more significant discriminative power in differentiating medical images. We evaluated our method on two public medical imaging datasets and it showed improved retrieval accuracy and efficiency.

Automatic Detection of the Uterus and Fallopian Tube Junctions in Laparoscopic Images

We present a method for the automatic detection of the uterus and the Fallopian tube/Uterus junctions (FU-junctions) in a monocular laparoscopic image. The main application is to perform automatic registration and fusion between preoperative radiological images of the uterus and laparoscopic images for image-guided surgery. In the broader context of computer assisted intervention, our method is the first that detects an organ and registration landmarks from laparoscopic images without manual input. Our detection problem is challenging because of the large inter-patient anatomical variability and pathologies such as uterine fibroids. We solve the problem using learned contextual geometric constraints that statistically model the positions and orientations of the FU-junctions relative to the uterus' body. We train the uterus detector using a modern part-based approach and the FU-junction detector using junction-specific context-sensitive features. We have trained and tested on a database of 95 uterus images with cross validation, and successfully detected the uterus with Recall = 0.95 and average Number of False Positives per Image (NFPI) = 0.21, and FU-junctions with Recall = 0.80 and NFPI = 0.50. Our experimental results show that the contextual constraints are fundamental to achieve high quality detection.

Pairwise Latent Semantic Association for Similarity Computation in Medical Imaging

Retrieving medical images that present similar diseases is an active research area for diagnostics and therapy. However, it can be problematic given the visual variations between anatomical structures. In this paper, we propose a new feature extraction method for similarity computation in medical imaging. Instead of the low-level visual appearance, we design a CCA-PairLDA feature representation method to capture the similarity between images with high-level semantics. First, we extract the PairLDA topics to represent an image as a mixture of latent semantic topics in an image pair context. Second, we generate a CCA-correlation model to represent the semantic association between an image pair for similarity computation. While PairLDA adjusts the latent topics for all image pairs, CCA-correlation helps to associate an individual image pair. In this way, the semantic descriptions of an image pair are closely correlated, and naturally correspond to similarity computation between images. We evaluated our method on two public medical imaging datasets for image retrieval and showed improved performance.

Large Margin Local Estimate With Applications to Medical Image Classification

Medical images usually exhibit large intra-class variation and inter-class ambiguity in the feature space, which could affect classification accuracy. To tackle this issue, we propose a new Large Margin Local Estimate (LMLE) classification model with sub-categorization based sparse representation. We first sub-categorize the reference sets of different classes into multiple clusters, to reduce feature variation within each subcategory compared to the entire reference set. Local estimates are generated for the test image using sparse representation with reference subcategories as the dictionaries. The similarity between the test image and each class is then computed by fusing the distances with the local estimates in a learning-based large margin aggregation construct to alleviate the problem of inter-class ambiguity. The derived similarities are finally used to determine the class label. We demonstrate that our LMLE model is generally applicable to different imaging modalities, and applied it to three tasks: interstitial lung disease (ILD) classification on high-resolution computed tomography (HRCT) images, phenotype binary classification and continuous regression on brain magnetic resonance (MR) imaging. Our experimental results show statistically significant performance improvements over existing popular classifiers.

Adaptive Cosegmentation of Pheochromocytomas in CECT Images Using Localized Level Set Models

Segmentation of pheochromocytomas in contrast-enhanced computed tomography (CECT) images is an ill-posed problem due to the presence of weak boundaries, intratumoral degeneration, and nearby structures and clutter. Additional information from different phases of CECT images needs to be imposed for better mass segmentations. In this paper, a novel adaptive cosegmentation method is proposed by incorporating a localized region-based level set model (LRLSM). The energy function is formulated with consideration of adaptive tradeoff between the complementary local information from image pairs. Gradient direction and shape dissimilarity measure are integrated to guide the level set evolution. Automatic localization radius selection is added to further facilitate the segmentation. Then, two level set functions from each image pair are evolved and refined alternately to minimize the energy function. Experimental results in 50 CECT image pairs show that the adaptive LRLSM-based method is effective in segmentation of pheochromocytoma at two phases and produces better results, especially in the cases with weak boundaries, and complex foreground and background.

Automated system for lung nodules classification based on wavelet feature descriptor and support vector machine

Background

Lung cancer is a leading cause of death worldwide; it refers to the uncontrolled growth of abnormal cells in the lung. A computed tomography (CT) scan of the thorax is the most sensitive method for detecting cancerous lung nodules. A lung nodule is a round lesion which can be either non-cancerous or cancerous. In the CT, the lung cancer is observed as round white shadow nodules. The possibility to obtain a manually accurate interpretation from CT scans demands a big effort by the radiologist and might be a fatiguing process. Therefore, the design of a computer-aided diagnosis (CADx) system would be helpful as a second opinion tool.

Methods

The stages of the proposed CADx are: a supervised extraction of the region of interest to eliminate the shape differences among CT images. The Daubechies db1, db2, and db4 wavelet transforms are computed with one and two levels of decomposition. After that, 19 features are computed from each wavelet sub-band. Then, the sub-band and attribute selection is performed. As a result, 11 features are selected and combined in pairs as inputs to the support vector machine (SVM), which is used to distinguish CT images containing cancerous nodules from those not containing nodules.

Results

The clinical data set used for experiments consists of 45 CT scans from ELCAP and LIDC. For the training stage 61 CT images were used (36 with cancerous lung nodules and 25 without lung nodules). The system performance was tested with 45 CT scans (23 CT scans with lung nodules and 22 without nodules), different from that used for training. The results obtained show that the methodology successfully classifies cancerous nodules with a diameter from 2 mm to 30 mm. The total preciseness obtained was 82%; the sensitivity was 90.90%, whereas the specificity was 73.91%.

Conclusions

The CADx system presented is competitive with other literature systems in terms of sensitivity. The system reduces the complexity of classification by not performing the typical segmentation stage of most CADx systems. Additionally, the novelty of the algorithm is the use of a wavelet feature descriptor.