A 3D nodule candidate detection method supported by hybrid features to reduce false positives in lung nodule detection

Naqi, Syed Muhammad; Sharif, Muhammad; Lali, Ikram Ullah

doi:10.1007/s11042-019-07819-3

Cited by 34 publications

(19 citation statements)

References 49 publications

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“…Radiomic texture features have been widely used in tissue characterization for discriminating different types of lesions in thoracic CT images, such as automatic lung nodule detection [28][29][30][31][32][33][34][35][36], differential diagnosis of benign and malignant lung nodules [15,16], and differentiation of lung cancer subtypes [17]. Several texture features have been suggested for automated detection of pulmonary nodules in CT images, including the mean, skewness, and kurtosis values of intensity histograms [28][29][30][31][32][33], local binary patterns [9,14,34,35], and gray-level co-occurrence matrix (GLCM)-based features [28][29][30]36]. While radiomic texture features were shown to be effective in distinguishing various types of lung nodules, discrimination between SPCH and LPA was intrinsically di cult due to their common GGN-like appearance.…”

Section: Discussionmentioning

confidence: 99%

Radiomic Feature Analysis for Distinguishing Solitary Pulmonary Capillary Hemangioma From Lepidic-Predominant Lung Adenocarcinoma in Ground Glass Lung Nodules

Lin¹,

Wang²,

Chen³

et al. 2020

Preprint

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Solitary pulmonary capillary hemangioma (SPCH) is a benign lung tumor that presents as ground-glass nodules (GGN) on computed tomography (CT) images, mimicking lepidic-predominant adenocarcinoma (LPA). This study aimed to establish a discriminant model using a radiomic feature analysis to distinguish SPCH from LPA in lung GGNs. This study included 13 and 49 patients who underwent complete resection for lung SPCH and LPA, respectively. An SPCH/LPA classification model was proposed based on a two-level decision tree and 26 radiomic features extracted from each segmented lesion, including 5 and 21 features from the histogram and co-occurrence matrix, respectively. The two-level decision tree was constructed based on the training data with a support vector machine (SVM) as the classifier in each tree node. For comparison, a baseline model was built with the same 26 features using an SVM as the classifier. Both models were assessed by the leave-one-out cross-validation method. The area under the receiver operating characteristic curve, accuracy, sensitivity, and specificity of the proposed SPCH/LPA model were 0.954, 91.9%, 92.3%, and 91.8%. The proposed SPCH/LPA model significantly outperformed the baseline model (p<0.05). Our results may help surgeons to preoperatively discriminate SPCH from LPA, thus avoiding unnecessary surgery for benign tumors.

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Section: Discussionmentioning

confidence: 99%

Radiomic Feature Analysis for Distinguishing Solitary Pulmonary Capillary Hemangioma From Lepidic-Predominant Lung Adenocarcinoma in Ground Glass Lung Nodules

Lin¹,

Wang²,

Chen³

et al. 2020

Preprint

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“…[166][167][168] showed algorithms that achieved better results that year. The deep learning methods [71,[169][170][171][172] for lung nodule detection did not show promising results.…”

Section: Lidc-idrimentioning

confidence: 96%

Survey on deep learning for pulmonary medical imaging

Song

Tian

et al. 2019

Front. Med.

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As a promising method in artificial intelligence, deep learning has been proven successful in several domains ranging from acoustics and images to natural language processing. With medical imaging becoming an important part of disease screening and diagnosis, deep learning-based approaches have emerged as powerful techniques in medical image areas. In this process, feature representations are learned directly and automatically from data, leading to remarkable breakthroughs in the medical field. Deep learning has been widely applied in medical imaging for improved image analysis. This paper reviews the major deep learning techniques in this time of rapid evolution and summarizes some of its key contributions and state-of-the-art outcomes. The topics include classification, detection, and segmentation tasks on medical image analysis with respect to pulmonary medical images, datasets, and benchmarks. A comprehensive overview of these methods implemented on various lung diseases consisting of pulmonary nodule diseases, pulmonary embolism, pneumonia, and interstitial lung disease is also provided. Lastly, the application of deep learning techniques to the medical image and an analysis of their future challenges and potential directions are discussed.

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“…Generally, thresholding, component analysis, region growing, morphological operations, and filtering [19], [22], [26], [51]- [52], [62], [67]- [68], [96], [109] are often used as rule-based approaches in preprocessing medical images. Thresholding and component analysis are the most effective and quick ways to approximately separate lung volume from distracting components.…”

Section: A Preprocessingmentioning

confidence: 99%

“…However, these classical image processing methods are developed according to pixel intensity and lowlevel representatives of images. Additional filtering or geometric features computing methods are also needed to optimize candidate nodule generation [22], [74].…”

Section: ) Candidate Nodule Detectionmentioning

confidence: 99%

A Comprehensive Review of Computer-Aided Diagnosis of Pulmonary Nodules Based on Computed Tomography Scans

Cao

et al. 2020

IEEE Access

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Lung cancer is one of the malignant tumor diseases with the fastest increase in morbidity and mortality, which poses a great threat to human health. Low-Dose Computed Tomography (LDCT) screening has been proved as a practical technique for improving the accuracy of pulmonary nodule detection and classification at early cancer diagnosis, which contributes to mortality reduction. Therefore, with the explosive growth of CT data, it is of great clinical significance to exploit an effective Computer-Aided Diagnosis (CAD) system for radiologists on automatic nodule analysis. In this paper, a comprehensive review of the application and development of CAD systems is presented. The experimental benchmarks for nodule analysis are first described and summarized, covering public datasets of lung CT scans, commonly used evaluation metrics, and various medical competitions. We then introduce the main structure of a CAD system and present some efficient methodologies. Due to the extensive use of Convolutional Neural Network (CNN)based methods in pulmonary nodule investigations recently, we summarized the advantages of CNNs over traditional image processing methods. Besides, we mainly select the CAD systems developed by state-of-theart CNNs with excellent performance and analyze their objectives, algorithms as well as results. Finally, research trends, existing challenges, and future directions in this field are discussed.

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A 3D nodule candidate detection method supported by hybrid features to reduce false positives in lung nodule detection

Cited by 34 publications

References 49 publications

Radiomic Feature Analysis for Distinguishing Solitary Pulmonary Capillary Hemangioma From Lepidic-Predominant Lung Adenocarcinoma in Ground Glass Lung Nodules

Radiomic Feature Analysis for Distinguishing Solitary Pulmonary Capillary Hemangioma From Lepidic-Predominant Lung Adenocarcinoma in Ground Glass Lung Nodules

Survey on deep learning for pulmonary medical imaging

A Comprehensive Review of Computer-Aided Diagnosis of Pulmonary Nodules Based on Computed Tomography Scans

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