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Masci GM, Chassagnon G, Alifano M, Tlemsani C, Boudou-Rouquette P, La Torre G, Calinghen A, Canniff E, Fournel L, Revel MP. Performance of AI for preoperative CT assessment of lung metastases: Retrospective analysis of 167 patients. Eur J Radiol 2024; 179:111667. [PMID: 39121746 DOI: 10.1016/j.ejrad.2024.111667] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2024] [Revised: 07/30/2024] [Accepted: 08/02/2024] [Indexed: 08/12/2024]
Abstract
OBJECTIVES To evaluate the performance of artificial intelligence (AI) in the preoperative detection of lung metastases on CT. MATERIALS AND METHODS Patients who underwent lung metastasectomy in our institution between 2016 and 2020 were enrolled, their preoperative CT reports having been performed before an AI solution (Veye Lung Nodules, version 3.9.2, Aidence) became available as a second reader in our department. All CT scans were retrospectively processed by AI. The sensitivities of unassisted radiologists (original CT radiology reports), AI reports alone and both combined were compared. Ground truth was established by a consensus reading of two radiologists, who analyzed whether the nodules mentioned in the pathology report were retrospectively visible on CT. Multivariate analysis was performed to identify nodule characteristics associated with detectability. RESULTS A total of 167 patients (men: 62.9 %; median age, 59 years [47-68]) with 475 resected nodules were included. AI detected an average of 4 nodules (0-17) per CT, of which 97 % were true nodules. The combination of radiologist plus AI (92.4 %) had significantly higher sensitivity than unassisted radiologists (80.4 %) (p < 0.001). In 27/57 (47.4 %) patients who had multiple preoperative CT scans, AI detected lung nodules earlier than the radiologist. Vascular contact was associated with non-detection by radiologists (OR:0.32[0.19, 0.54], p < 0.001), whilst the presence of cavitation (OR:0.26[0.13, 0.54], p < 0.001) or pleural contact (OR:0.10[0.04, 0.22], p < 0.001) was associated with non-detection by AI. CONCLUSION AI significantly increases the sensitivity of preoperative detection of lung metastases and enables earlier detection, with a significant potential benefit for patient management.
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Affiliation(s)
- Giorgio Maria Masci
- Radiology Department, Hôpital Cochin, AP-HP, 27 rue du Faubourg Saint-Jacques, 75014 Paris, France; Department of Radiological, Oncological and Pathological Sciences, Policlinico Umberto I, Università degli Studi di Roma La Sapienza, Viale del Policlinico 155, 00161 Rome, Italy
| | - Guillaume Chassagnon
- Radiology Department, Hôpital Cochin, AP-HP, 27 rue du Faubourg Saint-Jacques, 75014 Paris, France; Université de Paris Cité, 85 boulevard Saint-Germain, 75006 Paris, France
| | - Marco Alifano
- Université de Paris Cité, 85 boulevard Saint-Germain, 75006 Paris, France; Department of Thoracic Surgery, Hôpital Cochin, AP-HP, 27 rue du Faubourg Saint-Jacques, 75014 Paris, France
| | - Camille Tlemsani
- Université de Paris Cité, 85 boulevard Saint-Germain, 75006 Paris, France; Department of Medical Oncology, Hôpital Cochin, AP-HP, 27 rue du Faubourg Saint-Jacques, 75014 Paris, France
| | - Pascaline Boudou-Rouquette
- Department of Medical Oncology, Hôpital Cochin, AP-HP, 27 rue du Faubourg Saint-Jacques, 75014 Paris, France
| | - Giuseppe La Torre
- Department of Public Health and Infectious Diseases, Università degli Studi di Roma La Sapienza, Piazzale Aldo Moro 5, 00185 Rome, Italy
| | - Arvin Calinghen
- Radiology Department, Hôpital Cochin, AP-HP, 27 rue du Faubourg Saint-Jacques, 75014 Paris, France
| | - Emma Canniff
- Radiology Department, Hôpital Cochin, AP-HP, 27 rue du Faubourg Saint-Jacques, 75014 Paris, France
| | - Ludovic Fournel
- Université de Paris Cité, 85 boulevard Saint-Germain, 75006 Paris, France; Department of Thoracic Surgery, Hôpital Cochin, AP-HP, 27 rue du Faubourg Saint-Jacques, 75014 Paris, France
| | - Marie-Pierre Revel
- Radiology Department, Hôpital Cochin, AP-HP, 27 rue du Faubourg Saint-Jacques, 75014 Paris, France; Université de Paris Cité, 85 boulevard Saint-Germain, 75006 Paris, France.
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Riaz Z, Khan B, Abdullah S, Khan S, Islam MS. Lung Tumor Image Segmentation from Computer Tomography Images Using MobileNetV2 and Transfer Learning. Bioengineering (Basel) 2023; 10:981. [PMID: 37627866 PMCID: PMC10451633 DOI: 10.3390/bioengineering10080981] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Revised: 08/14/2023] [Accepted: 08/17/2023] [Indexed: 08/27/2023] Open
Abstract
BACKGROUND Lung cancer is one of the most fatal cancers worldwide, and malignant tumors are characterized by the growth of abnormal cells in the tissues of lungs. Usually, symptoms of lung cancer do not appear until it is already at an advanced stage. The proper segmentation of cancerous lesions in CT images is the primary method of detection towards achieving a completely automated diagnostic system. METHOD In this work, we developed an improved hybrid neural network via the fusion of two architectures, MobileNetV2 and UNET, for the semantic segmentation of malignant lung tumors from CT images. The transfer learning technique was employed and the pre-trained MobileNetV2 was utilized as an encoder of a conventional UNET model for feature extraction. The proposed network is an efficient segmentation approach that performs lightweight filtering to reduce computation and pointwise convolution for building more features. Skip connections were established with the Relu activation function for improving model convergence to connect the encoder layers of MobileNetv2 to decoder layers in UNET that allow the concatenation of feature maps with different resolutions from the encoder to decoder. Furthermore, the model was trained and fine-tuned on the training dataset acquired from the Medical Segmentation Decathlon (MSD) 2018 Challenge. RESULTS The proposed network was tested and evaluated on 25% of the dataset obtained from the MSD, and it achieved a dice score of 0.8793, recall of 0.8602 and precision of 0.93. It is pertinent to mention that our technique outperforms the current available networks, which have several phases of training and testing.
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Affiliation(s)
- Zainab Riaz
- Hong Kong Center for Cerebro-Cardiovascular Health Engineering (COCHE), Hong Kong SAR, China; (Z.R.); (B.K.); (M.S.I.)
| | - Bangul Khan
- Hong Kong Center for Cerebro-Cardiovascular Health Engineering (COCHE), Hong Kong SAR, China; (Z.R.); (B.K.); (M.S.I.)
- Department of Biomedical Engineering, City University Hongkong, Hong Kong SAR, China
| | - Saad Abdullah
- Division of Intelligent Future Technologies, School of Innovation, Design and Engineering, Mälardalen University, P.O. Box 883, 721 23 Västerås, Sweden
| | - Samiullah Khan
- Center for Eye & Vision Research, 17W Science Park, Hong Kong SAR, China;
| | - Md Shohidul Islam
- Hong Kong Center for Cerebro-Cardiovascular Health Engineering (COCHE), Hong Kong SAR, China; (Z.R.); (B.K.); (M.S.I.)
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Zhang Z, Tie Y, Zhang D, Liu F, Qi L. Quantum-Involution inspire false positive reduction in pulmonary nodule detection. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104850] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/19/2023]
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Park H, Yun J, Lee SM, Hwang HJ, Seo JB, Jung YJ, Hwang J, Lee SH, Lee SW, Kim N. Deep Learning-based Approach to Predict Pulmonary Function at Chest CT. Radiology 2023; 307:e221488. [PMID: 36786699 DOI: 10.1148/radiol.221488] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/15/2023]
Abstract
Background Low-dose chest CT screening is recommended for smokers with the potential for lung function abnormality, but its role in predicting lung function remains unclear. Purpose To develop a deep learning algorithm to predict pulmonary function with low-dose CT images in participants using health screening services. Materials and Methods In this retrospective study, participants underwent health screening with same-day low-dose CT and pulmonary function testing with spirometry at a university affiliated tertiary referral general hospital between January 2015 and December 2018. The data set was split into a development set (model training, validation, and internal test sets) and temporally independent test set according to first visit year. A convolutional neural network was trained to predict the forced expiratory volume in the first second of expiration (FEV1) and forced vital capacity (FVC) from low-dose CT. The mean absolute error and concordance correlation coefficient (CCC) were used to evaluate agreement between spirometry as the reference standard and deep-learning prediction as the index test. FVC and FEV1 percent predicted (hereafter, FVC% and FEV1%) values less than 80% and percent of FVC exhaled in first second (hereafter, FEV1/FVC) less than 70% were used to classify participants at high risk. Results A total of 16 148 participants were included (mean age, 55 years ± 10 [SD]; 10 981 men) and divided into a development set (n = 13 428) and temporally independent test set (n = 2720). In the temporally independent test set, the mean absolute error and CCC were 0.22 L and 0.94, respectively, for FVC and 0.22 L and 0.91 for FEV1. For the prediction of the respiratory high-risk group, FVC%, FEV1%, and FEV1/FVC had respective accuracies of 89.6% (2436 of 2720 participants; 95% CI: 88.4, 90.7), 85.9% (2337 of 2720 participants; 95% CI: 84.6, 87.2), and 90.2% (2453 of 2720 participants; 95% CI: 89.1, 91.3) in the same testing data set. The sensitivities were 61.6% (242 of 393 participants; 95% CI: 59.7, 63.4), 46.9% (226 of 482 participants; 95% CI: 45.0, 48.8), and 36.1% (91 of 252 participants; 95% CI: 34.3, 37.9), respectively. Conclusion A deep learning model applied to volumetric chest CT predicted pulmonary function with relatively good performance. © RSNA, 2023 Supplemental material is available for this article.
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Affiliation(s)
- Hyunjung Park
- From the Department of Medical Science and Department of Bioengineering, Asan Medical Institute of Convergence Science and Technology (H.P., N.K.), Department of Radiology and Research Institute of Radiology (J.Y., S.M.L., H.J.H., J.B.S., N.K.), Department of Pulmonology and Critical Care Medicine and Clinical Research Center for Chronic Obstructive Airway Diseases (S.W.L.), and Health Screening and Promotion Center (Y.J.J.), Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Republic of Korea; Division of Medical Oncology, Department of Internal Medicine, Korea University College of Medicine, Seoul, Republic of Korea (J.H.); Department of Biomedical Research Center, Korea University Guro Hospital, Seoul, Republic of Korea (J.H.); and Department of Pulmonology, Allergy and Critical Care Medicine, CHA Bundang Medical Center, CHA University, Seongnam, Republic of Korea (S.H.L.)
| | - Jihye Yun
- From the Department of Medical Science and Department of Bioengineering, Asan Medical Institute of Convergence Science and Technology (H.P., N.K.), Department of Radiology and Research Institute of Radiology (J.Y., S.M.L., H.J.H., J.B.S., N.K.), Department of Pulmonology and Critical Care Medicine and Clinical Research Center for Chronic Obstructive Airway Diseases (S.W.L.), and Health Screening and Promotion Center (Y.J.J.), Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Republic of Korea; Division of Medical Oncology, Department of Internal Medicine, Korea University College of Medicine, Seoul, Republic of Korea (J.H.); Department of Biomedical Research Center, Korea University Guro Hospital, Seoul, Republic of Korea (J.H.); and Department of Pulmonology, Allergy and Critical Care Medicine, CHA Bundang Medical Center, CHA University, Seongnam, Republic of Korea (S.H.L.)
| | - Sang Min Lee
- From the Department of Medical Science and Department of Bioengineering, Asan Medical Institute of Convergence Science and Technology (H.P., N.K.), Department of Radiology and Research Institute of Radiology (J.Y., S.M.L., H.J.H., J.B.S., N.K.), Department of Pulmonology and Critical Care Medicine and Clinical Research Center for Chronic Obstructive Airway Diseases (S.W.L.), and Health Screening and Promotion Center (Y.J.J.), Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Republic of Korea; Division of Medical Oncology, Department of Internal Medicine, Korea University College of Medicine, Seoul, Republic of Korea (J.H.); Department of Biomedical Research Center, Korea University Guro Hospital, Seoul, Republic of Korea (J.H.); and Department of Pulmonology, Allergy and Critical Care Medicine, CHA Bundang Medical Center, CHA University, Seongnam, Republic of Korea (S.H.L.)
| | - Hye Jeon Hwang
- From the Department of Medical Science and Department of Bioengineering, Asan Medical Institute of Convergence Science and Technology (H.P., N.K.), Department of Radiology and Research Institute of Radiology (J.Y., S.M.L., H.J.H., J.B.S., N.K.), Department of Pulmonology and Critical Care Medicine and Clinical Research Center for Chronic Obstructive Airway Diseases (S.W.L.), and Health Screening and Promotion Center (Y.J.J.), Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Republic of Korea; Division of Medical Oncology, Department of Internal Medicine, Korea University College of Medicine, Seoul, Republic of Korea (J.H.); Department of Biomedical Research Center, Korea University Guro Hospital, Seoul, Republic of Korea (J.H.); and Department of Pulmonology, Allergy and Critical Care Medicine, CHA Bundang Medical Center, CHA University, Seongnam, Republic of Korea (S.H.L.)
| | - Joon Beom Seo
- From the Department of Medical Science and Department of Bioengineering, Asan Medical Institute of Convergence Science and Technology (H.P., N.K.), Department of Radiology and Research Institute of Radiology (J.Y., S.M.L., H.J.H., J.B.S., N.K.), Department of Pulmonology and Critical Care Medicine and Clinical Research Center for Chronic Obstructive Airway Diseases (S.W.L.), and Health Screening and Promotion Center (Y.J.J.), Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Republic of Korea; Division of Medical Oncology, Department of Internal Medicine, Korea University College of Medicine, Seoul, Republic of Korea (J.H.); Department of Biomedical Research Center, Korea University Guro Hospital, Seoul, Republic of Korea (J.H.); and Department of Pulmonology, Allergy and Critical Care Medicine, CHA Bundang Medical Center, CHA University, Seongnam, Republic of Korea (S.H.L.)
| | - Young Ju Jung
- From the Department of Medical Science and Department of Bioengineering, Asan Medical Institute of Convergence Science and Technology (H.P., N.K.), Department of Radiology and Research Institute of Radiology (J.Y., S.M.L., H.J.H., J.B.S., N.K.), Department of Pulmonology and Critical Care Medicine and Clinical Research Center for Chronic Obstructive Airway Diseases (S.W.L.), and Health Screening and Promotion Center (Y.J.J.), Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Republic of Korea; Division of Medical Oncology, Department of Internal Medicine, Korea University College of Medicine, Seoul, Republic of Korea (J.H.); Department of Biomedical Research Center, Korea University Guro Hospital, Seoul, Republic of Korea (J.H.); and Department of Pulmonology, Allergy and Critical Care Medicine, CHA Bundang Medical Center, CHA University, Seongnam, Republic of Korea (S.H.L.)
| | - Jeongeun Hwang
- From the Department of Medical Science and Department of Bioengineering, Asan Medical Institute of Convergence Science and Technology (H.P., N.K.), Department of Radiology and Research Institute of Radiology (J.Y., S.M.L., H.J.H., J.B.S., N.K.), Department of Pulmonology and Critical Care Medicine and Clinical Research Center for Chronic Obstructive Airway Diseases (S.W.L.), and Health Screening and Promotion Center (Y.J.J.), Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Republic of Korea; Division of Medical Oncology, Department of Internal Medicine, Korea University College of Medicine, Seoul, Republic of Korea (J.H.); Department of Biomedical Research Center, Korea University Guro Hospital, Seoul, Republic of Korea (J.H.); and Department of Pulmonology, Allergy and Critical Care Medicine, CHA Bundang Medical Center, CHA University, Seongnam, Republic of Korea (S.H.L.)
| | - Se Hee Lee
- From the Department of Medical Science and Department of Bioengineering, Asan Medical Institute of Convergence Science and Technology (H.P., N.K.), Department of Radiology and Research Institute of Radiology (J.Y., S.M.L., H.J.H., J.B.S., N.K.), Department of Pulmonology and Critical Care Medicine and Clinical Research Center for Chronic Obstructive Airway Diseases (S.W.L.), and Health Screening and Promotion Center (Y.J.J.), Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Republic of Korea; Division of Medical Oncology, Department of Internal Medicine, Korea University College of Medicine, Seoul, Republic of Korea (J.H.); Department of Biomedical Research Center, Korea University Guro Hospital, Seoul, Republic of Korea (J.H.); and Department of Pulmonology, Allergy and Critical Care Medicine, CHA Bundang Medical Center, CHA University, Seongnam, Republic of Korea (S.H.L.)
| | - Sei Won Lee
- From the Department of Medical Science and Department of Bioengineering, Asan Medical Institute of Convergence Science and Technology (H.P., N.K.), Department of Radiology and Research Institute of Radiology (J.Y., S.M.L., H.J.H., J.B.S., N.K.), Department of Pulmonology and Critical Care Medicine and Clinical Research Center for Chronic Obstructive Airway Diseases (S.W.L.), and Health Screening and Promotion Center (Y.J.J.), Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Republic of Korea; Division of Medical Oncology, Department of Internal Medicine, Korea University College of Medicine, Seoul, Republic of Korea (J.H.); Department of Biomedical Research Center, Korea University Guro Hospital, Seoul, Republic of Korea (J.H.); and Department of Pulmonology, Allergy and Critical Care Medicine, CHA Bundang Medical Center, CHA University, Seongnam, Republic of Korea (S.H.L.)
| | - Namkug Kim
- From the Department of Medical Science and Department of Bioengineering, Asan Medical Institute of Convergence Science and Technology (H.P., N.K.), Department of Radiology and Research Institute of Radiology (J.Y., S.M.L., H.J.H., J.B.S., N.K.), Department of Pulmonology and Critical Care Medicine and Clinical Research Center for Chronic Obstructive Airway Diseases (S.W.L.), and Health Screening and Promotion Center (Y.J.J.), Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Republic of Korea; Division of Medical Oncology, Department of Internal Medicine, Korea University College of Medicine, Seoul, Republic of Korea (J.H.); Department of Biomedical Research Center, Korea University Guro Hospital, Seoul, Republic of Korea (J.H.); and Department of Pulmonology, Allergy and Critical Care Medicine, CHA Bundang Medical Center, CHA University, Seongnam, Republic of Korea (S.H.L.)
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Afriyie Y, Weyori BA, Opoku AA. A scaling up approach: a research agenda for medical imaging analysis with applications in deep learning. J EXP THEOR ARTIF IN 2023. [DOI: 10.1080/0952813x.2023.2165721] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Affiliation(s)
- Yaw Afriyie
- Department of Computer Science and Informatics, University of Energy and Natural Resources, School of Sciences, Sunyani, Ghana
- Department of Computer Science, Faculty of Information and Communication Technology, SD Dombo University of Business and Integrated Development Studies, Wa, Ghana
| | - Benjamin A. Weyori
- Department of Computer Science and Informatics, University of Energy and Natural Resources, School of Sciences, Sunyani, Ghana
| | - Alex A. Opoku
- Department of Mathematics & Statistics, University of Energy and Natural Resources, School of Sciences, Sunyani, Ghana
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Lung Cancer Nodules Detection via an Adaptive Boosting Algorithm Based on Self-Normalized Multiview Convolutional Neural Network. JOURNAL OF ONCOLOGY 2022; 2022:5682451. [PMID: 36199795 PMCID: PMC9529389 DOI: 10.1155/2022/5682451] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Revised: 06/28/2022] [Accepted: 07/19/2022] [Indexed: 11/18/2022]
Abstract
Lung cancer is the deadliest cancer killing almost 1.8 million people in 2020. The new cases are expanding alarmingly. Early lung cancer manifests itself in the form of nodules in the lungs. One of the most widely used techniques for both lung cancer early and noninvasive diagnosis is computed tomography (CT). However, the intensive workload of radiologists to read a large number of scans for nodules detection gives rise to issues like false detection and missed detection. To overcome these issues, we proposed an innovative strategy titled adaptive boosting self-normalized multiview convolution neural network (AdaBoost-SNMV-CNN) for lung cancer nodules detection across CT scans. In AdaBoost-SNMV-CNN, MV-CNN function as a baseline learner while the scaled exponential linear unit (SELU) activation function normalizes the layers by considering their neighbors' information and a special drop-out technique (α-dropout). The proposed method was trained and tested using the widely Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) and Early Lung Cancer Action Program (ELCAP) datasets. AdaBoost-SNMV-CNN achieved an accuracy of 92%, sensitivity of 93%, and specificity of 92% for lung nodules detection on the LIDC-IDRI dataset. Meanwhile, on the ELCAP dataset, the accuracy for detecting lung nodules was 99%, sensitivity 100%, and specificity 98%. AdaBoost-SNMV-CNN outperformed the majority of the model in accuracy, sensitivity, and specificity. The multiviews confer the model's good generalization and learning ability for diverse features of lung nodules, the model architecture is simple, and has a minimal computational time of around 102 minutes. We believe that AdaBoost-SNMV-CNN has good accuracy for the detection of lung nodules and anticipate its potential application in the noninvasive clinical diagnosis of lung cancer. This model can be of good assistance to the radiologist and will be of interest to researchers involved in the designing and development of advanced systems for the detection of lung nodules to accomplish the goal of noninvasive diagnosis of lung cancer.
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Tyagi S, Talbar SN. CSE-GAN: A 3D conditional generative adversarial network with concurrent squeeze-and-excitation blocks for lung nodule segmentation. Comput Biol Med 2022; 147:105781. [DOI: 10.1016/j.compbiomed.2022.105781] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2022] [Revised: 06/16/2022] [Accepted: 06/19/2022] [Indexed: 11/03/2022]
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The 3D Position Estimation and Tracking of a Surface Vehicle Using a Mono-Camera and Machine Learning. ELECTRONICS 2022. [DOI: 10.3390/electronics11142141] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The ability to obtain the 3D position of target vehicles is essential to managing and coordinating a multi-robot operation. We investigate an ML-backed object localization and tracking system to estimate the target’s 3D position based on a mono-camera input. The passive vision-only technique provides a robust field awareness in challenging conditions such as GPS-denied or radio-silent environments. Our processing pipeline utilizes a YOLOv5 neural network as the back-end detection module and a temporal filtering technique to improve detection and tracking accuracy. The filtering process effectively removes false positive labels to improve tracking accuracy. We propose a piecewise projection model to predict the target 3D position from the estimated 2D bounding box. Our projection model utilizes the co-plane property of ground vehicles to calculate 2D–3D mapping. Experimental results show that the piecewise model is more accurate than existing methods when the training dataset is not evenly distributed in the sampling space. Our piecewise model outperforms the singular RANSAC-based and the 6DPose methods by 28% in location errors. A less than 10-m error is observed for most near-to-mid-range cases.
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Fahmy D, Kandil H, Khelifi A, Yaghi M, Ghazal M, Sharafeldeen A, Mahmoud A, El-Baz A. How AI Can Help in the Diagnostic Dilemma of Pulmonary Nodules. Cancers (Basel) 2022; 14:cancers14071840. [PMID: 35406614 PMCID: PMC8997734 DOI: 10.3390/cancers14071840] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Revised: 03/29/2022] [Accepted: 03/30/2022] [Indexed: 02/04/2023] Open
Abstract
Simple Summary Pulmonary nodules are considered a sign of bronchogenic carcinoma, detecting them early will reduce their progression and can save lives. Lung cancer is the second most common type of cancer in both men and women. This manuscript discusses the current applications of artificial intelligence (AI) in lung segmentation as well as pulmonary nodule segmentation and classification using computed tomography (CT) scans, published in the last two decades, in addition to the limitations and future prospects in the field of AI. Abstract Pulmonary nodules are the precursors of bronchogenic carcinoma, its early detection facilitates early treatment which save a lot of lives. Unfortunately, pulmonary nodule detection and classification are liable to subjective variations with high rate of missing small cancerous lesions which opens the way for implementation of artificial intelligence (AI) and computer aided diagnosis (CAD) systems. The field of deep learning and neural networks is expanding every day with new models designed to overcome diagnostic problems and provide more applicable and simply used models. We aim in this review to briefly discuss the current applications of AI in lung segmentation, pulmonary nodule detection and classification.
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Affiliation(s)
- Dalia Fahmy
- Diagnostic Radiology Department, Mansoura University Hospital, Mansoura 35516, Egypt;
| | - Heba Kandil
- Bioengineering Department, University of Louisville, Louisville, KY 40292, USA; (H.K.); (A.S.); (A.M.)
- Information Technology Department, Faculty of Computers and Informatics, Mansoura University, Mansoura 35516, Egypt
| | - Adel Khelifi
- Computer Science and Information Technology Department, Abu Dhabi University, Abu Dhabi 59911, United Arab Emirates;
| | - Maha Yaghi
- Electrical, Computer, and Biomedical Engineering Department, Abu Dhabi University, Abu Dhabi 59911, United Arab Emirates; (M.Y.); (M.G.)
| | - Mohammed Ghazal
- Electrical, Computer, and Biomedical Engineering Department, Abu Dhabi University, Abu Dhabi 59911, United Arab Emirates; (M.Y.); (M.G.)
| | - Ahmed Sharafeldeen
- Bioengineering Department, University of Louisville, Louisville, KY 40292, USA; (H.K.); (A.S.); (A.M.)
| | - Ali Mahmoud
- Bioengineering Department, University of Louisville, Louisville, KY 40292, USA; (H.K.); (A.S.); (A.M.)
| | - Ayman El-Baz
- Bioengineering Department, University of Louisville, Louisville, KY 40292, USA; (H.K.); (A.S.); (A.M.)
- Correspondence:
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Deep Learning Applications in Computed Tomography Images for Pulmonary Nodule Detection and Diagnosis: A Review. Diagnostics (Basel) 2022; 12:diagnostics12020298. [PMID: 35204388 PMCID: PMC8871398 DOI: 10.3390/diagnostics12020298] [Citation(s) in RCA: 34] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Revised: 01/21/2022] [Accepted: 01/22/2022] [Indexed: 12/26/2022] Open
Abstract
Lung cancer has one of the highest mortality rates of all cancers and poses a severe threat to people’s health. Therefore, diagnosing lung nodules at an early stage is crucial to improving patient survival rates. Numerous computer-aided diagnosis (CAD) systems have been developed to detect and classify such nodules in their early stages. Currently, CAD systems for pulmonary nodules comprise data acquisition, pre-processing, lung segmentation, nodule detection, false-positive reduction, segmentation, and classification. A number of review articles have considered various components of such systems, but this review focuses on segmentation and classification parts. Specifically, categorizing segmentation parts based on lung nodule type and network architectures, i.e., general neural network and multiview convolution neural network (CNN) architecture. Moreover, this work organizes related literature for classification of parts based on nodule or non-nodule and benign or malignant. The essential CT lung datasets and evaluation metrics used in the detection and diagnosis of lung nodules have been systematically summarized as well. Thus, this review provides a baseline understanding of the topic for interested readers.
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Bhatt SD, Soni HB. Improving Classification Accuracy of Pulmonary Nodules using Simplified Deep Neural Network. Open Biomed Eng J 2021. [DOI: 10.2174/1874120702115010180] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
Background:
Lung cancer is among the major causes of death in the world. Early detection of lung cancer is a major challenge. These encouraged the development of Computer-Aided Detection (CAD) system.
Objectives:
We designed a CAD system for performance improvement in detecting and classifying pulmonary nodules. Though the system will not replace radiologists, it will be helpful to them in order to accurately diagnose lung cancer.
Methods:
The architecture comprises of two steps, among which in the first step CT scans are pre-processed and the candidates are extracted using the positive and negative annotations provided along with the LUNA16 dataset, and the second step consists of three different neural networks for classifying the pulmonary nodules obtained from the first step. The models in the second step consist of 2D-Convolutional Neural Network (2D-CNN), Visual Geometry Group-16 (VGG-16) and simplified VGG-16, which independently classify pulmonary nodules.
Results:
The classification accuracies achieved for 2D-CNN, VGG-16 and simplified VGG-16 were 99.12%, 98.17% and 99.60%, respectively.
Conclusion:
The integration of deep learning techniques along with machine learning and image processing can serve as a good means of extracting pulmonary nodules and classifying them with improved accuracy. Based on these results, it can be concluded that the transfer learning concept will improve system performance. In addition, performance improves proper designing of the CAD system by considering the amount of dataset and the availability of computing power.
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12
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Lung Nodule Detection from Feature Engineering to Deep Learning in Thoracic CT Images: a Comprehensive Review. J Digit Imaging 2021; 33:655-677. [PMID: 31997045 DOI: 10.1007/s10278-020-00320-6] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
This paper presents a systematic review of the literature focused on the lung nodule detection in chest computed tomography (CT) images. Manual detection of lung nodules by the radiologist is a sequential and time-consuming process. The detection is subjective and depends on the radiologist's experiences. Owing to the variation in shapes and appearances of a lung nodule, it is very difficult to identify the proper location of the nodule from a huge number of slices generated by the CT scanner. Small nodules (< 10 mm in diameter) may be missed by this manual detection process. Therefore, computer-aided diagnosis (CAD) system acts as a "second opinion" for the radiologists, by making final decision quickly with higher accuracy and greater confidence. The goal of this survey work is to present the current state of the artworks and their progress towards lung nodule detection to the researchers and readers in this domain. This review paper has covered the published works from 2009 to April 2018. Different nodule detection approaches are described elaborately in this work. Recently, it is observed that deep learning (DL)-based approaches are applied extensively for nodule detection and characterization. Therefore, emphasis has been given to convolutional neural network (CNN)-based DL approaches by describing different CNN-based networks.
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13
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Liu W, Liu X, Li H, Li M, Zhao X, Zhu Z. Integrating Lung Parenchyma Segmentation and Nodule Detection With Deep Multi-Task Learning. IEEE J Biomed Health Inform 2021; 25:3073-3081. [PMID: 33471772 DOI: 10.1109/jbhi.2021.3053023] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Lung parenchyma segmentation is valuable for improving the performance of lung nodule detection in computed tomography (CT) images. Traditionally, the two tasks are performed separately. This paper proposes a deep multi-task learning (MTL) approach to integrate these tasks for better lung nodule detection. Three new ideas lead to our proposed approach. First, lung parenchyma segmentation is used as the attention module and is combined with nodule detection in a single deep network. Second, lung nodule detection is performed in an anchor-free manner by dividing it into two subtasks, nodule center identification and nodule size regression. Third, a novel pyramid dilated convolution block (PDCB) is proposed to utilize the advantage of dilated convolution and tackle its gridding problem for better lung parenchyma segmentation. Based on these ideas, we design our end-to-end deep network architecture and corresponding MTL method to achieve lung parenchyma segmentation and nodule detection simultaneously. We evaluate the proposed approach on the commonly used Lung Nodule Analysis 2016 (LUNA16) dataset. The experimental results show the value of our contributions and demonstrate that our approach can yield significant improvements compared with state-of-the-art counterparts.
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14
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Quantitative laryngoscopy with computer-aided diagnostic system for laryngeal lesions. Sci Rep 2021; 11:10147. [PMID: 33980940 PMCID: PMC8115147 DOI: 10.1038/s41598-021-89680-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Accepted: 04/22/2021] [Indexed: 12/05/2022] Open
Abstract
Laryngoscopes are widely used in the clinical diagnosis of laryngeal lesions, but such diagnosis relies heavily on the physician's subjective experience. The purpose of this study was to develop a computer-aided diagnostic system for the detection of laryngeal lesions based on objective criteria. This study used the distinct features of the image contour to find the clearest image in the laryngoscopic video. First to reduce the illumination problem caused by the laryngoscope lens, which could not fix the position of the light source, this study proposed image compensation to provide the image with a consistent brightness range for better performance. Second, we also proposed a method to automatically screen clear images from laryngoscopic film. Third, we used ACM to segment automatically them based on structural features of the pharynx and larynx, using hue and geometric analysis in the vocal cords and other zones. Finally, the support vector machine was used to classify laryngeal lesions based on a decision tree. This study evaluated the performance of the proposed system by assessing the laryngeal images of 284 patients. The accuracy of the detection for vocal cord polyps, cysts, leukoplakia, tumors, and healthy vocal cords were 93.15%, 95.16%, 100%, 96.42%, and 100%, respectively. The cross-validation accuracy for the five classes were 93.1%, 94.95%, 99.4%, 96.01% and 100%, respectively, and the average test accuracy for the laryngeal lesions was 93.33%. Our results showed that it was feasible to take the hue and geometric features of the larynx as signs to identify laryngeal lesions and that they could effectively assist physicians in diagnosing laryngeal lesions.
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15
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Salient detection network for lung nodule detection in 3D Thoracic MRI Images. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2020.102404] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
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16
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Sun L, Wang Z, Pu H, Yuan G, Guo L, Pu T, Peng Z. Attention-embedded complementary-stream CNN for false positive reduction in pulmonary nodule detection. Comput Biol Med 2021; 133:104357. [PMID: 33836449 DOI: 10.1016/j.compbiomed.2021.104357] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Revised: 03/22/2021] [Accepted: 03/22/2021] [Indexed: 01/18/2023]
Abstract
False positive reduction plays a key role in computer-aided detection systems for pulmonary nodule detection in computed tomography (CT) scans. However, this remains a challenge owing to the heterogeneity and similarity of anisotropic pulmonary nodules. In this study, a novel attention-embedded complementary-stream convolutional neural network (AECS-CNN) is proposed to obtain more representative features of nodules for false positive reduction. The proposed network comprises three function blocks: 1) attention-guided multi-scale feature extraction, 2) complementary-stream block with an attention module for feature integration, and 3) classification block. The inputs of the network are multi-scale 3D CT volumes due to variations in nodule sizes. Subsequently, a gradual multi-scale feature extraction block with an attention module was applied to acquire more contextual information regarding the nodules. A subsequent complementary-stream integration block with an attention module was utilized to learn the significantly complementary features. Finally, the candidates were classified using a fully connected layer block. An exhaustive experiment on the LUNA16 challenge dataset was conducted to verify the effectiveness and performance of the proposed network. The AECS-CNN achieved a sensitivity of 0.92 with 4 false positives per scan. The results indicate that the attention mechanism can improve the network performance in false positive reduction, the proposed AECS-CNN can learn more representative features, and the attention module can guide the network to learn the discriminated feature channels and the crucial information embedded in the data, thereby effectively enhancing the performance of the detection system.
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Affiliation(s)
- Lingma Sun
- School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, 610054, China; Laboratory of Imaging Detection and Intelligent Perception, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Zhuoran Wang
- School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, 610054, China; Laboratory of Imaging Detection and Intelligent Perception, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Hong Pu
- Sichuan Provincial People's Hospital, Chengdu, Sichuan, 610072, China; School of Medicine, University of Electronic Science and Technology of China, Chengdu, Sichuan, 610054, China
| | - Guohui Yuan
- School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, 610054, China; Laboratory of Imaging Detection and Intelligent Perception, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Lu Guo
- Sichuan Provincial People's Hospital, Chengdu, Sichuan, 610072, China; School of Medicine, University of Electronic Science and Technology of China, Chengdu, Sichuan, 610054, China
| | - Tian Pu
- School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, 610054, China; Laboratory of Imaging Detection and Intelligent Perception, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Zhenming Peng
- School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, 610054, China; Laboratory of Imaging Detection and Intelligent Perception, University of Electronic Science and Technology of China, Chengdu, 611731, China.
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Chen L, Gu D, Chen Y, Shao Y, Cao X, Liu G, Gao Y, Wang Q, Shen D. An artificial-intelligence lung imaging analysis system (ALIAS) for population-based nodule computing in CT scans. Comput Med Imaging Graph 2021; 89:101899. [PMID: 33761446 DOI: 10.1016/j.compmedimag.2021.101899] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2020] [Revised: 12/15/2020] [Accepted: 03/08/2021] [Indexed: 12/24/2022]
Abstract
Computed tomography (CT) screening is essential for early lung cancer detection. With the development of artificial intelligence techniques, it is particularly desirable to explore the ability of current state-of-the-art methods and to analyze nodule features in terms of a large population. In this paper, we present an artificial-intelligence lung image analysis system (ALIAS) for nodule detection and segmentation. And after segmenting the nodules, the locations, sizes, as well as imaging features are computed at the population level for studying the differences between benign and malignant nodules. The results provide better understanding of the underlying imaging features and their ability for early lung cancer diagnosis.
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Affiliation(s)
- Liyun Chen
- Shanghai Jiao Tong University, Shanghai, China; Shanghai United Imaging Intelligence Co. Ltd, Shanghai, China
| | - Dongdong Gu
- Hunan University, Changsha, Hunan, China; Shanghai United Imaging Intelligence Co. Ltd, Shanghai, China
| | - Yanbo Chen
- Shanghai United Imaging Intelligence Co. Ltd, Shanghai, China
| | - Ying Shao
- Shanghai United Imaging Intelligence Co. Ltd, Shanghai, China
| | - Xiaohuan Cao
- Shanghai United Imaging Intelligence Co. Ltd, Shanghai, China
| | - Guocai Liu
- Hunan University, Changsha, Hunan, China
| | - Yaozong Gao
- Shanghai United Imaging Intelligence Co. Ltd, Shanghai, China
| | - Qian Wang
- Shanghai Jiao Tong University, Shanghai, China.
| | - Dinggang Shen
- Shanghai United Imaging Intelligence Co. Ltd, Shanghai, China.
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Perl RM, Grimmer R, Hepp T, Horger MS. Can a Novel Deep Neural Network Improve the Computer-Aided Detection of Solid Pulmonary Nodules and the Rate of False-Positive Findings in Comparison to an Established Machine Learning Computer-Aided Detection? Invest Radiol 2021; 56:103-108. [PMID: 32796198 DOI: 10.1097/rli.0000000000000713] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
OBJECTIVE The aim of this study was to compare the performance of 2 approved computer-aided detection (CAD) systems for detection of pulmonary solid nodules (PSNs) in an oncologic cohort. The first CAD system is based on a conventional machine learning approach (VD10F), and the other is based on a deep 3D convolutional neural network (CNN) CAD software (VD20A). METHODS AND MATERIALS Nine hundred sixty-seven patients with a total of 2451 PSNs were retrospectively evaluated using the 2 different CAD systems. All patients had thin-slice chest computed tomography (0.6 mm) using 100 kV and 100 mAs and a high-resolution kernel (I50f). The CAD images generated by VD10F were transferred to the PACS for evaluation. The images generated by VD20A were evaluated using a Web browser-based viewer. Finally, a senior radiologist who was blinded for the CAD results examined the thin-slice images of every patient (ground truth). RESULTS A total of 2451 PSNs were detected by the senior radiologist. CAD-VD10F detected 1401 true-positive, 143 false-negative, 565 false-positive (FP), and 342 true-negative PSNs, resulting in sensitivity of 90.7%, specificity of 37.7%, positive predictive value of 0.71, and negative predictive value of 0.70. CAD-VD20A detected 1381 true-positive, 163 false-negative, 337 FP, and 570 true-negative PSNs, resulting in sensitivity of 89.4%, specificity of 62.8%, positive predictive value of 0.80, and negative predictive value 0.77, respectively. The rate of FP per scan was 0.6 for CAD-VD10F and 0.3 for CAD-VD20A. CONCLUSIONS The new deep learning-based CAD software (VD20A) shows similar sensitivity with the conventional CAD software (VD10F), but a significantly higher specificity.
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Affiliation(s)
- Regine Mariette Perl
- From the Department of Diagnostic and Interventional Radiology, University Hospital of Tuebingen, Tuebingen
| | | | | | - Marius Stefan Horger
- From the Department of Diagnostic and Interventional Radiology, University Hospital of Tuebingen, Tuebingen
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Spiculation Sign Recognition in a Pulmonary Nodule Based on Spiking Neural P Systems. BIOMED RESEARCH INTERNATIONAL 2020; 2020:6619076. [PMID: 33426059 PMCID: PMC7775132 DOI: 10.1155/2020/6619076] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/10/2020] [Revised: 12/04/2020] [Accepted: 12/11/2020] [Indexed: 11/18/2022]
Abstract
The spiculation sign is one of the main signs to distinguish benign and malignant pulmonary nodules. In order to effectively extract the image feature of a pulmonary nodule for the spiculation sign distinguishment, a new spiculation sign recognition model is proposed based on the doctors' diagnosis process of pulmonary nodules. A maximum density projection model is established to fuse the local three-dimensional information into the two-dimensional image. The complete boundary of a pulmonary nodule is extracted by the improved Snake model, which can take full advantage of the parallel calculation of the Spike Neural P Systems to build a new neural network structure. In this paper, our experiments show that the proposed algorithm can accurately extract the boundary of a pulmonary nodule and effectively improve the recognition rate of the spiculation sign.
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20
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Halder A, Chatterjee S, Dey D, Kole S, Munshi S. An adaptive morphology based segmentation technique for lung nodule detection in thoracic CT image. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020; 197:105720. [PMID: 32877818 DOI: 10.1016/j.cmpb.2020.105720] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/25/2020] [Accepted: 08/19/2020] [Indexed: 05/13/2023]
Abstract
Lung cancer is one of the most life-threatening cancers mostly indicated by the presence of nodules in the lung. Doctors and radiological experts use High-Resolution Computed Tomography (HRCT) images for nodule detection and further decision making from visual inspection. Manual detection of lung nodules is a time-consuming process. Therefore, Computer-aided detection (CADe) systems have been developed for accurate nodule detection and segmentation. CADe-based systems assist radiologists to detect lung nodules with greater confidence and a lesser amount of time and have a significant impact on the accurate, uniform, and early-stage diagnosis of lung cancer. In this research work, an adaptive morphology-based segmentation technique (AMST) has been introduced by designing an adaptive morphological filter for improved segmentation of the lung nodule region. The adaptive morphological filter detects candidate nodule regions by employing adaptive structuring element (ASE) and at the same time improves nodule detection accuracy by reducing false positives (FPs) from the Computed Tomography (CT) slices. The detected nodule candidate regions are then processed for feature extraction. In this study, morphological, texture and intensity-based features have been used with support vector machine (SVM) classifier for lung nodule detection. The performance of the proposed framework has been evaluated by incorporating a 10-fold cross-validation technique on Lung Image Database Consortium-Image Database Resource Initiative (LIDC/IDRI) dataset and on a private dataset, collected from a consultant radiologist. It has been observed that the proposed automated computer-aided detection system has achieved overall classification performance indices with 94.88% sensitivity, 93.45% specificity and 94.27% detection accuracy with 1.8 FPs/scan on LIDC/IDRI dataset and 91.43% sensitivity, 90.45% specificity, 92.83% accuracy with 3.2 FPs/scan on a private dataset. The results show that the proposed CADe system presented in this paper outperforms the other state-of-the-art methods for automatic nodule detection from the HRCT image.
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Affiliation(s)
- Amitava Halder
- Computer Science and Engineering Department, Supreme Knowledge Foundation Group of Institutions, Hooghly 712139, India.
| | | | - Debangshu Dey
- Electrical Engineering Department, Jadavpur University, Kolkata 700032, India
| | - Surajit Kole
- Theism Ultrasound Centre, 14 B Dumdum Rd., Kolkata 700030, India
| | - Sugata Munshi
- Electrical Engineering Department, Jadavpur University, Kolkata 700032, India
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21
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Liu C, Hu SC, Wang C, Lafata K, Yin FF. Automatic detection of pulmonary nodules on CT images with YOLOv3: development and evaluation using simulated and patient data. Quant Imaging Med Surg 2020; 10:1917-1929. [PMID: 33014725 PMCID: PMC7495314 DOI: 10.21037/qims-19-883] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2019] [Accepted: 06/29/2020] [Indexed: 12/15/2022]
Abstract
BACKGROUND To develop a high-efficiency pulmonary nodule computer-aided detection (CAD) method for localization and diameter estimation. METHODS The developed CAD method centralizes a novel convolutional neural network (CNN) algorithm, You Only Look Once (YOLO) v3, as a deep learning approach. This method is featured by two distinct properties: (I) an automatic multi-scale feature extractor for nodule feature screening, and (II) a feature-based bounding box generator for nodule localization and diameter estimation. Two independent studies were performed to train and evaluate this CAD method. One study comprised of a computer simulation that utilized computer-based ground truth. In this study, 300 CT scans were simulated by Cardiac-torso (XCAT) digital phantom. Spherical nodules of various sizes (i.e., 3-10 mm in diameter) were randomly implanted within the lung region of the simulated images-the second study utilized human-based ground truth in patients. The CAD method was developed by CT scans sourced from the LIDC-IDRI database. CT scans with slice thickness above 2.5 mm were excluded, leaving 888 CT images for analysis. A 10-fold cross-validation procedure was implemented in both studies to evaluate network hyper-parameterization and generalization. The overall accuracy of the CAD method was evaluated by the detection sensitivities, in response to average false positives (FPs) per image. In the patient study, the detection accuracy was further compared against 9 recently published CAD studies using free-receiver response operating characteristic (FROC) curve analysis. Localization and diameter estimation accuracies were quantified by the mean and standard error between the predicted value and ground truth. RESULTS The average results among the 10 cross-validation folds in both studies demonstrated the CAD method achieved high detection accuracy. The sensitivity was 99.3% (FPs =1), and improved to 100% (FPs =4) in the simulation study. The corresponding sensitivities were 90.0% and 95.4% in the patient study, displaying superiority over several conventional and CNN-based lung nodule CAD methods in the FROC curve analysis. Nodule localization and diameter estimation errors were less than 1 mm in both studies. The developed CAD method achieved high computational efficiency: it yields nodule-specific quantitative values (i.e., number, existence confidence, central coordinates, and diameter) within 0.1 s for 2D CT slice inputs. CONCLUSIONS The reported results suggest that the developed lung pulmonary nodule CAD method possesses high accuracies of nodule localization and diameter estimation. The high computational efficiency enables its potential clinical application in the future.
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Affiliation(s)
- Chenyang Liu
- Medical Physics Graduate Program, Duke Kunshan University, Kunshan, China
| | - Shen-Chiang Hu
- Medical Physics Graduate Program, Duke Kunshan University, Kunshan, China
| | - Chunhao Wang
- Department of Radiation Oncology, Duke University Medical Center, Durham, NC, USA
| | - Kyle Lafata
- Department of Radiation Oncology, Duke University Medical Center, Durham, NC, USA
| | - Fang-Fang Yin
- Medical Physics Graduate Program, Duke Kunshan University, Kunshan, China
- Department of Radiation Oncology, Duke University Medical Center, Durham, NC, USA
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Meldo A, Utkin L, Kovalev M, Kasimov E. The natural language explanation algorithms for the lung cancer computer-aided diagnosis system. Artif Intell Med 2020; 108:101952. [PMID: 32972653 DOI: 10.1016/j.artmed.2020.101952] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2020] [Revised: 07/16/2020] [Accepted: 08/19/2020] [Indexed: 12/20/2022]
Abstract
Two algorithms for explaining decisions of a lung cancer computer-aided diagnosis system are proposed. Their main peculiarity is that they produce explanations of diseases in the form of special sentences via natural language. The algorithms consist of two parts. The first part is a standard local post-hoc explanation model, for example, the well-known LIME, which is used for selecting important features from a special feature representation of the segmented lung suspicious objects. This part is identical for both algorithms. The second part is a model which aims to connect selected important features and to transform them to explanation sentences in natural language. This part is implemented differently for both algorithms. The training phase of the first algorithm uses a special vocabulary of simple phrases which produce sentences and their embeddings. The second algorithm significantly simplifies some parts of the first algorithm and reduces the explanation problem to a set of simple classifiers. The basic idea behind the improvement is to represent every simple phrase from vocabulary as a class of the "sparse" histograms. An implementation of the second algorithm is shown in detail.
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Affiliation(s)
- Anna Meldo
- Peter the Great St. Petersburg Polytechnic University (SPbPU), Russia; Clinical Research Center of Specialized Types of Medical Care (Oncological), Russia
| | - Lev Utkin
- Peter the Great St. Petersburg Polytechnic University (SPbPU), Russia.
| | - Maxim Kovalev
- Peter the Great St. Petersburg Polytechnic University (SPbPU), Russia
| | - Ernest Kasimov
- Peter the Great St. Petersburg Polytechnic University (SPbPU), Russia
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Yu J, Yang B, Wang J, Leader J, Wilson D, Pu J. 2D CNN versus 3D CNN for false-positive reduction in lung cancer screening. J Med Imaging (Bellingham) 2020; 7:051202. [PMID: 33062802 PMCID: PMC7550796 DOI: 10.1117/1.jmi.7.5.051202] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2020] [Accepted: 09/28/2020] [Indexed: 11/14/2022] Open
Abstract
Purpose: To clarify whether and to what extent three-dimensional (3D) convolutional neural network (CNN) is superior to 2D CNN when applied to reduce false-positive nodule detections in the scenario of low-dose computed tomography (CT) lung cancer screening. Approach: We established a dataset consisting of 1600 chest CT examinations acquired on different subjects from various sources. There were in total 18,280 candidate nodules in these CT examinations, among which 9185 were nodules and 9095 were not nodules. For each candidate nodule, we extracted a number of cubic subvolumes with a dimension of 72 × 72 × 72 mm 3 by rotating the CT examinations randomly for 25 times prior to the extraction of the axis-aligned subvolumes. These subvolumes were split into three groups in a ratio of 8 ∶ 1 ∶ 1 for training, validation, and independent testing purposes. We developed a multiscale CNN architecture and implemented its 2D and 3D versions to classify pulmonary nodules into two categories, namely true positive and false positive. The performance of the 2D/3D-CNN classification schemes was evaluated using the area under the receiver operating characteristic curves (AUC). The p -values and the 95% confidence intervals (CI) were calculated. Results: The AUC for the optimal 2D-CNN model is 0.9307 (95% CI: 0.9285 to 0.9330) with a sensitivity of 92.70% and a specificity of 76.21%. The 3D-CNN model with the best performance had an AUC of 0.9541 (95% CI: 0.9495 to 0.9583) with a sensitivity of 89.98% and a specificity of 87.30%. The developed multiscale CNN architecture had a better performance than the vanilla architecture did. Conclusions: The 3D-CNN model has a better performance in false-positive reduction compared with its 2D counterpart; however, the improvement is relatively limited and demands more computational resources for training purposes.
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Affiliation(s)
- Juezhao Yu
- University of Pittsburgh, Departments of Radiology and Bioengineering, Pittsburgh, Pennsylvania, United States
| | - Bohan Yang
- University of Pittsburgh, Departments of Radiology and Bioengineering, Pittsburgh, Pennsylvania, United States
| | - Jing Wang
- University of Pittsburgh, Departments of Radiology and Bioengineering, Pittsburgh, Pennsylvania, United States
| | - Joseph Leader
- University of Pittsburgh, Departments of Radiology and Bioengineering, Pittsburgh, Pennsylvania, United States
| | - David Wilson
- University of Pittsburgh, Department of Medicine, Pittsburgh, Pennsylvania, United States
| | - Jiantao Pu
- University of Pittsburgh, Departments of Radiology and Bioengineering, Pittsburgh, Pennsylvania, United States
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Kuo CFJ, Kao CH, Dlamini S, Liu SC. Laryngopharyngeal reflux image quantization and analysis of its severity. Sci Rep 2020; 10:10975. [PMID: 32620899 PMCID: PMC7335083 DOI: 10.1038/s41598-020-67587-1] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2019] [Accepted: 06/10/2020] [Indexed: 12/21/2022] Open
Abstract
Laryngopharyngeal reflux (LPR) is a prevalent disease affecting a high proportion of patients seeking laryngology consultation. Diagnosis is made subjectively based on history, symptoms, and endoscopic assessment. The results depend on the examiner's interpretation of endoscopic images. There are still no consistent objective diagnostic methods. The aim of this study is to use image processing techniques to quantize the laryngeal variation caused by LPR, to judge and analyze its severity. This study proposed methods of screening sharp images automatically from laryngeal endoscopic images and using throat eigen structure for automatic region segmentation. The proposed image compensation improved the illumination problems from the use of laryngoscope lens. Fisher linear discriminant was used to find out features and classification performance while support vector machine was used as the classifier for judging LPR. Evaluation results were 97.16% accuracy, 98.11% sensitivity, and 3.77% false positive rate. To evaluate the severity, quantized data of the laryngeal variation was used. LPR images were combined with reflux symptom index score chart, and severity was graded using a neural network. The results indicated 96.08% accuracy. The experiment indicated that laryngeal variation induced by LPR could be quantized by using image processing techniques to assist in diagnosing and treating LPR.
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Affiliation(s)
- Chung-Feng Jeffrey Kuo
- Department of Material Science and Engineering, National Taiwan University of Science and Technology, No. 43, Sec. 4, Keelung Road, Da'an District, Taipei, Taiwan, ROC
| | - Chih-Hsiang Kao
- Department of Material Science and Engineering, National Taiwan University of Science and Technology, No. 43, Sec. 4, Keelung Road, Da'an District, Taipei, Taiwan, ROC
| | - Sifundvolesihle Dlamini
- Department of Material Science and Engineering, National Taiwan University of Science and Technology, No. 43, Sec. 4, Keelung Road, Da'an District, Taipei, Taiwan, ROC
| | - Shao-Cheng Liu
- Department of Otolaryngology-Head and Neck Surgery, Tri-Service General Hospital, National Defense Medical Center, No. 325, Sec. 2, Cheng-Gong Road, Neihu District, Taipei, 114, Taiwan, ROC.
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25
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Fu B, Wang G, Wu M, Li W, Zheng Y, Chu Z, Lv F. Influence of CT effective dose and convolution kernel on the detection of pulmonary nodules in different artificial intelligence software systems: A phantom study. Eur J Radiol 2020; 126:108928. [DOI: 10.1016/j.ejrad.2020.108928] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2019] [Revised: 01/05/2020] [Accepted: 02/28/2020] [Indexed: 12/29/2022]
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Automatic Detection of Pulmonary Nodules using Three-dimensional Chain Coding and Optimized Random Forest. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10072346] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
The detection of pulmonary nodules on computed tomography scans provides a clue for the early diagnosis of lung cancer. Manual detection mandates a heavy radiological workload as it identifies nodules slice-by-slice. This paper presents a fully automated nodule detection with three significant contributions. First, an automated seeded region growing is designed to segment the lung regions from the tomography scans. Second, a three-dimensional chain code algorithm is implemented to refine the border of the segmented lungs. Lastly, nodules inside the lungs are detected using an optimized random forest classifier. The experiments for our proposed detection are conducted using 888 scans from a public dataset, and achieves a favorable result of 93.11% accuracy, 94.86% sensitivity, and 91.37% specificity, with only 0.0863 false positives per exam.
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Zheng S, Guo J, Cui X, Veldhuis RNJ, Oudkerk M, van Ooijen PMA. Automatic Pulmonary Nodule Detection in CT Scans Using Convolutional Neural Networks Based on Maximum Intensity Projection. IEEE TRANSACTIONS ON MEDICAL IMAGING 2020; 39:797-805. [PMID: 31425026 DOI: 10.1109/tmi.2019.2935553] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/21/2023]
Abstract
Accurate pulmonary nodule detection is a crucial step in lung cancer screening. Computer-aided detection (CAD) systems are not routinely used by radiologists for pulmonary nodule detection in clinical practice despite their potential benefits. Maximum intensity projection (MIP) images improve the detection of pulmonary nodules in radiological evaluation with computed tomography (CT) scans. Inspired by the clinical methodology of radiologists, we aim to explore the feasibility of applying MIP images to improve the effectiveness of automatic lung nodule detection using convolutional neural networks (CNNs). We propose a CNN-based approach that takes MIP images of different slab thicknesses (5 mm, 10 mm, 15 mm) and 1 mm axial section slices as input. Such an approach augments the two-dimensional (2-D) CT slice images with more representative spatial information that helps discriminate nodules from vessels through their morphologies. Our proposed method achieves sensitivity of 92.7% with 1 false positive per scan and sensitivity of 94.2% with 2 false positives per scan for lung nodule detection on 888 scans in the LIDC-IDRI dataset. The use of thick MIP images helps the detection of small pulmonary nodules (3 mm-10 mm) and results in fewer false positives. Experimental results show that utilizing MIP images can increase the sensitivity and lower the number of false positives, which demonstrates the effectiveness and significance of the proposed MIP-based CNNs framework for automatic pulmonary nodule detection in CT scans. The proposed method also shows the potential that CNNs could gain benefits for nodule detection by combining the clinical procedure.
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Spectral analysis for pulmonary nodule detection using the optimal fractional S-Transform. Comput Biol Med 2020; 119:103675. [PMID: 32339120 DOI: 10.1016/j.compbiomed.2020.103675] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2019] [Revised: 02/23/2020] [Accepted: 02/23/2020] [Indexed: 01/18/2023]
Abstract
Different frequency components of the lung, which have not been fully considered in traditional computer-aided detection systems for pulmonary nodules, can cause heterogeneous energy distribution. Hence, spectral analysis, which is an important time-frequency representation tool, is utilized to characterize the frequency-dependent energy responses of nodules. In this study, a novel spectral-analysis-based method for nodule candidate detection is presented. The optimal fractional S-transform is applied to transform raw computed tomography images from the spatial to time-frequency domain. Next, a time-frequency cube is decomposed using spectral decomposition to a frequency-dependent energy slice. Subsequently, an energy distribution is obtained by the Teager-Kaiser energy (TKE) to characterize the nodules. Finally, nodule candidates are detected using rule-based and threshold algorithms in the TKE image. The proposed method is validated on a clinical CT data set from Sichuan Provincial People's Hospital. The signal-to-clutter ratio (SCR) increases by 35.5% with respect to raw CT slices. Furthermore, the proposed method exhibits a sensitivity of 97.87%, with only 6.8 false positives per slice. The total number of nodule candidates has an average reduction of 50%. The results indicate that the time-frequency features can effectively characterize solid nodules. Moreover, the proposed method demonstrates accurate detection and can reduce the number of false positive efficiently.
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Kuo CFJ, Huang CC, Siao JJ, Hsieh CW, Huy VQ, Ko KH, Hsu HH. Automatic lung nodule detection system using image processing techniques in computed tomography. Biomed Signal Process Control 2020. [DOI: 10.1016/j.bspc.2019.101659] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
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Abstract
Detecting malignant lung nodules from computed tomography (CT) scans is a hard and time-consuming task for radiologists. To alleviate this burden, computer-aided diagnosis (CAD) systems have been proposed. In recent years, deep learning approaches have shown impressive results outperforming classical methods in various fields. Nowadays, researchers are trying different deep learning techniques to increase the performance of CAD systems in lung cancer screening with computed tomography. In this work, we review recent state-of-the-art deep learning algorithms and architectures proposed as CAD systems for lung cancer detection. They are divided into two categories—(1) Nodule detection systems, which from the original CT scan detect candidate nodules; and (2) False positive reduction systems, which from a set of given candidate nodules classify them into benign or malignant tumors. The main characteristics of the different techniques are presented, and their performance is analyzed. The CT lung datasets available for research are also introduced. Comparison between the different techniques is presented and discussed.
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Cao K, Meng G, Wang Z, Liu Y, Liu H, Sun L. An adaptive pulmonary nodule detection algorithm. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2020; 28:427-447. [PMID: 32333576 DOI: 10.3233/xst-200656] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Recently, lung cancer has been paid more and more attention. People have reached a consensus that early detection and early treatment can improve the survival rate of patients. Among them, pulmonary nodules are the important reference for doctors to determine the lung health. With the continuous improvement of CT image resolution, more suspected pulmonary nodule information appears from the impact of chest CT. How to relatively and accurately locate the suspected nodule location from a large number of CT images has brought challenges to the doctor's daily diagnosis. To solve the problem that the original DBSCAN clustering algorithm needs manual setting of the threshold, this paper proposes a region growing algorithm and an adaptive DBSCAN clustering algorithm to improve the accuracy of pulmonary nodule detection. The image is roughly processed and ROI (Regions of Interest) region is roughly extracted by CLAHE transform. The region growing algorithm is used to roughly process the adjacent region's expansibility and the suspected region in ROI, and mark the center point in the region and the boundary point of its point set. The mean value of region range is taken as the threshold value of DBSCAN clustering algorithm. The center of the point domain is used as the starting point of clustering, and the rough set of points is used as the MinPts threshold. Finally, the clustering results are labeled in the initial CT image. Experiments show that the pulmonary nodule detection method proposed in this paper effectively improves the accuracy of the detection results.
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Affiliation(s)
- Keyan Cao
- College of Information and Control Engineering, Shenyang Jianzhu University, Shenyang, China
- Liaoning Province Big Data Management and Analysis Laboratory of Urban Construction, Shenyang, China
| | - Gongjie Meng
- College of Information and Control Engineering, Shenyang Jianzhu University, Shenyang, China
| | - Zhiqiong Wang
- College of Medicine and Biological Information Engineering, Northeast University, Shenyang, China
| | - Yefan Liu
- College of Information and Control Engineering, Shenyang Jianzhu University, Shenyang, China
| | - Haoli Liu
- College of Information and Control Engineering, Shenyang Jianzhu University, Shenyang, China
| | - Liangliang Sun
- College of Information and Control Engineering, Shenyang Jianzhu University, Shenyang, China
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Gruetzemacher R, Gupta A, Paradice D. 3D deep learning for detecting pulmonary nodules in CT scans. J Am Med Inform Assoc 2019; 25:1301-1310. [PMID: 30137371 DOI: 10.1093/jamia/ocy098] [Citation(s) in RCA: 39] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2017] [Accepted: 07/03/2018] [Indexed: 01/09/2023] Open
Abstract
Objective To demonstrate and test the validity of a novel deep-learning-based system for the automated detection of pulmonary nodules. Materials and Methods The proposed system uses 2 3D deep learning models, 1 for each of the essential tasks of computer-aided nodule detection: candidate generation and false positive reduction. A total of 888 scans from the LIDC-IDRI dataset were used for training and evaluation. Results Results for candidate generation on the test data indicated a detection rate of 94.77% with 30.39 false positives per scan, while the test results for false positive reduction exhibited a sensitivity of 94.21% with 1.789 false positives per scan. The overall system detection rate on the test data was 89.29% with 1.789 false positives per scan. Discussion An extensive and rigorous validation was conducted to assess the performance of the proposed system. The system demonstrated a novel combination of 3D deep neural network architectures and demonstrates the use of deep learning for both candidate generation and false positive reduction to be evaluated with a substantial test dataset. The results strongly support the ability of deep learning pulmonary nodule detection systems to generalize to unseen data. The source code and trained model weights have been made available. Conclusion A novel deep-neural-network-based pulmonary nodule detection system is demonstrated and validated. The results provide comparison of the proposed deep-learning-based system over other similar systems based on performance.
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Affiliation(s)
- Ross Gruetzemacher
- Department of Systems & Technology, Raymond J. Harbert College of Business, Auburn University, Auburn, AL, USA 36849
| | - Ashish Gupta
- Department of Systems & Technology, Raymond J. Harbert College of Business, Auburn University, Auburn, AL, USA 36849
| | - David Paradice
- Department of Systems & Technology, Raymond J. Harbert College of Business, Auburn University, Auburn, AL, USA 36849
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Gao Y, Tan J, Liang Z, Li L, Huo Y. Improved computer-aided detection of pulmonary nodules via deep learning in the sinogram domain. Vis Comput Ind Biomed Art 2019; 2:15. [PMID: 32240409 PMCID: PMC7099542 DOI: 10.1186/s42492-019-0029-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2019] [Accepted: 10/16/2019] [Indexed: 12/02/2022] Open
Abstract
Computer aided detection (CADe) of pulmonary nodules plays an important role in assisting radiologists’ diagnosis and alleviating interpretation burden for lung cancer. Current CADe systems, aiming at simulating radiologists’ examination procedure, are built upon computer tomography (CT) images with feature extraction for detection and diagnosis. Human visual perception in CT image is reconstructed from sinogram, which is the original raw data acquired from CT scanner. In this work, different from the conventional image based CADe system, we propose a novel sinogram based CADe system in which the full projection information is used to explore additional effective features of nodules in the sinogram domain. Facing the challenges of limited research in this concept and unknown effective features in the sinogram domain, we design a new CADe system that utilizes the self-learning power of the convolutional neural network to learn and extract effective features from sinogram. The proposed system was validated on 208 patient cases from the publicly available online Lung Image Database Consortium database, with each case having at least one juxtapleural nodule annotation. Experimental results demonstrated that our proposed method obtained a value of 0.91 of the area under the curve (AUC) of receiver operating characteristic based on sinogram alone, comparing to 0.89 based on CT image alone. Moreover, a combination of sinogram and CT image could further improve the value of AUC to 0.92. This study indicates that pulmonary nodule detection in the sinogram domain is feasible with deep learning.
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Affiliation(s)
- Yongfeng Gao
- Department of Radiology, State University of New York, Stony Brook, NY, 11794, USA
| | - Jiaxing Tan
- Department of Radiology, State University of New York, Stony Brook, NY, 11794, USA.,Departments of Computer Science, City University of New York/CSI, Staten Island, NY, 10314, USA
| | - Zhengrong Liang
- Department of Radiology, State University of New York, Stony Brook, NY, 11794, USA.
| | - Lihong Li
- Engineering and Environmental Science, City University of New York/CSI, Staten Island,, NY, 10314, USA
| | - Yumei Huo
- Departments of Computer Science, City University of New York/CSI, Staten Island, NY, 10314, USA
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Shaukat F, Raja G, Frangi AF. Computer-aided detection of lung nodules: a review. J Med Imaging (Bellingham) 2019. [DOI: 10.1117/1.jmi.6.2.020901] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Affiliation(s)
- Furqan Shaukat
- University of Engineering and Technology, Department of Electrical Engineering, Taxila
| | - Gulistan Raja
- University of Engineering and Technology, Department of Electrical Engineering, Taxila
| | - Alejandro F. Frangi
- University of Leeds Woodhouse Lane, School of Computing and School of Medicine, Leeds
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Saba T. Automated lung nodule detection and classification based on multiple classifiers voting. Microsc Res Tech 2019; 82:1601-1609. [DOI: 10.1002/jemt.23326] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2018] [Revised: 03/30/2019] [Accepted: 06/08/2019] [Indexed: 01/06/2023]
Affiliation(s)
- Tanzila Saba
- College of Computer and Information SciencesPrince Sultan University Riyadh Saudi Arabia
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36
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Chen G, Zhang J, Zhuo D, Pan Y, Pang C. Identification of pulmonary nodules via CT images with hierarchical fully convolutional networks. Med Biol Eng Comput 2019; 57:1567-1580. [DOI: 10.1007/s11517-019-01976-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2018] [Accepted: 03/27/2019] [Indexed: 10/27/2022]
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Pehrson LM, Nielsen MB, Ammitzbøl Lauridsen C. Automatic Pulmonary Nodule Detection Applying Deep Learning or Machine Learning Algorithms to the LIDC-IDRI Database: A Systematic Review. Diagnostics (Basel) 2019; 9:E29. [PMID: 30866425 PMCID: PMC6468920 DOI: 10.3390/diagnostics9010029] [Citation(s) in RCA: 47] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2018] [Revised: 01/29/2019] [Accepted: 02/19/2019] [Indexed: 12/27/2022] Open
Abstract
The aim of this study was to provide an overview of the literature available on machine learning (ML) algorithms applied to the Lung Image Database Consortium Image Collection (LIDC-IDRI) database as a tool for the optimization of detecting lung nodules in thoracic CT scans. This systematic review was compiled according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Only original research articles concerning algorithms applied to the LIDC-IDRI database were included. The initial search yielded 1972 publications after removing duplicates, and 41 of these articles were included in this study. The articles were divided into two subcategories describing their overall architecture. The majority of feature-based algorithms achieved an accuracy >90% compared to the deep learning (DL) algorithms that achieved an accuracy in the range of 82.2%⁻97.6%. In conclusion, ML and DL algorithms are able to detect lung nodules with a high level of accuracy, sensitivity, and specificity using ML, when applied to an annotated archive of CT scans of the lung. However, there is no consensus on the method applied to determine the efficiency of ML algorithms.
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Affiliation(s)
- Lea Marie Pehrson
- Department of Diagnostic Radiology, Copenhagen University Hospital, Rigshospitalet, 2100 Copenhagen, Denmark.
- Department of Technology, Faculty of Health and Technology, University College Copenhagen, 2200 Copenhagen, Denmark.
| | - Michael Bachmann Nielsen
- Department of Diagnostic Radiology, Copenhagen University Hospital, Rigshospitalet, 2100 Copenhagen, Denmark.
| | - Carsten Ammitzbøl Lauridsen
- Department of Diagnostic Radiology, Copenhagen University Hospital, Rigshospitalet, 2100 Copenhagen, Denmark.
- Department of Technology, Faculty of Health and Technology, University College Copenhagen, 2200 Copenhagen, Denmark.
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38
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Wang T, Gong J, Duan HH, Wang LJ, Ye XD, Nie SD. Correlation between CT based radiomics features and gene expression data in non-small cell lung cancer. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2019; 27:773-803. [PMID: 31450540 DOI: 10.3233/xst-190526] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
OBJECTIVE Radiogenomics investigates radiographic imaging phenotypes associated with gene expression patterns. This study aims to explore relationships between CT imaging radiomics features and gene expression data in non-small cell lung cancer (NSCLC). METHODS Eighty-nine NSCLC patients are included in the study. Radiomics features are extracted and selected to quantify the phenotype of tumors on CT-scans. Co-expressed genes are also clustered and the first principal component of the cluster is represented, which is defined as a metagene. Then, statistical analysis was performed to assess association of CT radiomics features with metagenes. In addition, predictive models are built and metagene enrichment are conducted to further evaluate performance of NSCLC radiogenomics statistically and biologically. RESULTS There are 187 significant pairwise correlations between a CT radiomics feature and a metagene of NSCLC, where eighteen metagenes are annotated with Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) terms. Metagenes are predicted in terms of radiomics features with an accuracy of 41.89% -89.93%. CONCLUSIONS This study reveals the associations between CT imaging radiomics features and NSCLC co-expressed gene sets. The findings suggest that CT radiomics features can reflect important biological information of NSCLC patients, which may have a significant clinical impact as CT is routinely used in clinical practice, assisting in improving medical decision-support at low cost.
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Affiliation(s)
- Ting Wang
- School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Jing Gong
- School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Hui-Hong Duan
- School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Li-Jia Wang
- School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Xiao-Dan Ye
- Department of Radiology, Shanghai Jiao Tong University Affiliated Chest Hospital, Shanghai, China
| | - Sheng-Dong Nie
- School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai, China
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Tan J, Huo Y, Liang Z, Li L. Expert knowledge-infused deep learning for automatic lung nodule detection. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2019; 27:17-35. [PMID: 30452432 PMCID: PMC6453714 DOI: 10.3233/xst-180426] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/15/2023]
Abstract
BACKGROUND Computer aided detection (CADe) of pulmonary nodules from computed tomography (CT) is crucial for early diagnosis of lung cancer. Self-learned features obtained by training datasets via deep learning have facilitated CADe of the nodules. However, the complexity of CT lung images renders a challenge of extracting effective features by self-learning only. This condition is exacerbated for limited size of datasets. On the other hand, the engineered features have been widely studied. OBJECTIVE We proposed a novel nodule CADe which aims to relieve the challenge by the use of available engineered features to prevent convolution neural networks (CNN) from overfitting under dataset limitation and reduce the running-time complexity of self-learning. METHODS The CADe methodology infuses adequately the engineered features, particularly texture features, into the deep learning process. RESULTS The methodology was validated on 208 patients with at least one juxta-pleural nodule from the public LIDC-IDRI database. Results demonstrated that the methodology achieves a sensitivity of 88% with 1.9 false positives per scan and a sensitivity of 94.01% with 4.01 false positives per scan. CONCLUSIONS The methodology shows high performance compared with the state-of-the-art results, in terms of accuracy and efficiency, from both existing CNN-based approaches and engineered feature-based classifications.
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Affiliation(s)
- Jiaxing Tan
- Department of Computer Science, City University of New York, the Graduate Center, NY, USA
| | - Yumei Huo
- Department of Computer Science, City University of New York at CSI, NY, USA
| | - Zhengrong Liang
- Department of Radiology, State University of New York at Stony Brook, NY, USA
- Corresponding author: Zhengrong Liang, Department of Radiology, Electrical and Computer Engineering, and Biomedical Engineering, Stony Brook University, Stony Brook, NY 11794, USA. .
| | - Lihong Li
- Department of Engineering Science and Physics, City University of New York at CSI, NY, USA
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40
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NODULe: Combining constrained multi-scale LoG filters with densely dilated 3D deep convolutional neural network for pulmonary nodule detection. Neurocomputing 2018. [DOI: 10.1016/j.neucom.2018.08.022] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
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41
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Lung nodule detection and classification based on geometric fit in parametric form and deep learning. Neural Comput Appl 2018. [DOI: 10.1007/s00521-018-3773-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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42
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da Silva GLF, Valente TLA, Silva AC, de Paiva AC, Gattass M. Convolutional neural network-based PSO for lung nodule false positive reduction on CT images. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2018; 162:109-118. [PMID: 29903476 DOI: 10.1016/j.cmpb.2018.05.006] [Citation(s) in RCA: 50] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/01/2017] [Revised: 09/15/2017] [Accepted: 05/03/2018] [Indexed: 06/08/2023]
Abstract
BACKGROUND AND OBJECTIVE Detection of lung nodules is critical in CAD systems; this is because of their similar contrast with other structures and low density, which result in the generation of numerous false positives (FPs). Therefore, this study proposes a methodology to reduce the FP number using a deep learning technique in conjunction with an evolutionary technique. METHOD The particle swarm optimization (PSO) algorithm was used to optimize the network hyperparameters in the convolutional neural network (CNN) in order to enhance the network performance and eliminate the requirement of manual search. RESULTS The methodology was tested on computed tomography (CT) scans from the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) with the highest accuracy of 97.62%, sensitivity of 92.20%, specificity of 98.64%, and area under the receiver operating characteristic (ROC) curve of 0.955. CONCLUSION The results demonstrate the high performance-potential of the PSO algorithm in the identification of optimal CNN hyperparameters for lung nodule candidate classification into nodules and non-nodules, increasing the sensitivity rates in the FP reduction step of CAD systems.
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Affiliation(s)
- Giovanni Lucca França da Silva
- Federal University of Maranhão - UFMA, Applied Computing Group - NCA Av. dos Portugueses, SN, Bacanga, São Luís, MA 65085-580, Brazil.
| | - Thales Levi Azevedo Valente
- Pontifical Catholic University of Rio de Janeiro - PUC - Rio R. São Vicente, 225, Gávea, Rio de Janeiro, RJ 22453-900, Brazil.
| | - Aristófanes Corrêa Silva
- Federal University of Maranhão - UFMA, Applied Computing Group - NCA Av. dos Portugueses, SN, Bacanga, São Luís, MA 65085-580, Brazil.
| | - Anselmo Cardoso de Paiva
- Federal University of Maranhão - UFMA, Applied Computing Group - NCA Av. dos Portugueses, SN, Bacanga, São Luís, MA 65085-580, Brazil.
| | - Marcelo Gattass
- Pontifical Catholic University of Rio de Janeiro - PUC - Rio R. São Vicente, 225, Gávea, Rio de Janeiro, RJ 22453-900, Brazil.
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Saad M, Choi TS. Deciphering unclassified tumors of non-small-cell lung cancer through radiomics. Comput Biol Med 2018; 91:222-230. [PMID: 29100116 DOI: 10.1016/j.compbiomed.2017.10.029] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2017] [Revised: 10/24/2017] [Accepted: 10/24/2017] [Indexed: 01/14/2023]
Abstract
BACKGROUND Tumors are highly heterogeneous at the phenotypic, physiologic, and genomic levels. They are categorized in terms of a differentiated appearance under a microscope. Non-small-cell lung cancer tumors are categorized into three main subgroups: adenocarcinoma, squamous cell carcinoma, and large cell carcinoma. In approximately 20% of pathology reports, they are returned unclassified or classified as not-otherwise-specified (NOS) owing to scant materials or poor tumor differentiation. METHOD We present a radiomic interrogation of molecular spatial variations to decode unclassified NOS tumor architecture quantitatively. Twelve spatial descriptors with various displacements and directions were extracted and profiled with respect to the subgroups. The profiled descriptors were used to decipher the NOS tumor morphologic clues from the imaging phenotype perspective. This profiler was built as an extended version of a conventional support-vector-machine classifier, wherein a genetic algorithm and correlation analysis were embedded to define the molecular signatures of poorly differentiated tumors using well-differentiated-tumor information. RESULTS Sixteen multi-class classifier models with an 81% average accuracy and descriptor subset size ranging from 12 to 144 were reported. The average area under the curve was 86.3% at a 95% confidence interval and a 0.03-0.08 standard error. Correlation analysis returned an unclassified NOS membership matrix with respect to the cell-architecture similarity score for the subgroups. The best model demonstrated 53% NOS reduction. CONCLUSION The membership matrix is expected to assist pathologists and oncologists in cases of unresectable tumors or scant biopsy materials for histological subtyping and cancer therapy.
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Affiliation(s)
- Maliazurina Saad
- School of Mechatronics, Gwangju Institute of Science and Technology, 123 Cheomdan-gwagiro, Oryong-Dong, Buk-gu, Gwangju 61005, South Korea.
| | - Tae-Sun Choi
- School of Mechatronics, Gwangju Institute of Science and Technology, 123 Cheomdan-gwagiro, Oryong-Dong, Buk-gu, Gwangju 61005, South Korea.
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Computer-assisted subtyping and prognosis for non-small cell lung cancer patients with unresectable tumor. Comput Med Imaging Graph 2018; 67:1-8. [DOI: 10.1016/j.compmedimag.2018.04.003] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2017] [Revised: 03/10/2018] [Accepted: 04/02/2018] [Indexed: 11/21/2022]
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45
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A novel pixel value space statistics map of the pulmonary nodule for classification in computerized tomography images. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2018; 2017:556-559. [PMID: 29059933 DOI: 10.1109/embc.2017.8036885] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
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.
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Chung H, Ko H, Jeon SJ, Yoon KH, Lee J. Automatic Lung Segmentation With Juxta-Pleural Nodule Identification Using Active Contour Model and Bayesian Approach. IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE-JTEHM 2018; 6:1800513. [PMID: 29910995 PMCID: PMC6001848 DOI: 10.1109/jtehm.2018.2837901] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/24/2017] [Revised: 03/28/2018] [Accepted: 05/06/2018] [Indexed: 11/07/2022]
Abstract
OBJECTIVE chest computed tomography (CT) images and their quantitative analyses have become increasingly important for a variety of purposes, including lung parenchyma density analysis, airway analysis, diaphragm mechanics analysis, and nodule detection for cancer screening. Lung segmentation is an important prerequisite step for automatic image analysis. We propose a novel lung segmentation method to minimize the juxta-pleural nodule issue, a notorious challenge in the applications. METHOD we initially used the Chan-Vese (CV) model for active lung contours and adopted a Bayesian approach based on the CV model results, which predicts the lung image based on the segmented lung contour in the previous frame image or neighboring upper frame image. Among the resultant juxta-pleural nodule candidates, false positives were eliminated through concave points detection and circle/ellipse Hough transform. Finally, the lung contour was modified by adding the final nodule candidates to the area of the CV model results. RESULTS to evaluate the proposed method, we collected chest CT digital imaging and communications in medicine images of 84 anonymous subjects, including 42 subjects with juxta-pleural nodules. There were 16 873 images in total. Among the images, 314 included juxta-pleural nodules. Our method exhibited a disc similarity coefficient of 0.9809, modified hausdorff distance of 0.4806, sensitivity of 0.9785, specificity of 0.9981, accuracy of 0.9964, and juxta-pleural nodule detection rate of 96%. It outperformed existing methods, such as the CV model used alone, the normalized CV model, and the snake algorithm. Clinical impact: the high accuracy with the juxta-pleural nodule detection in the lung segmentation can be beneficial for any computer aided diagnosis system that uses lung segmentation as an initial step.
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Affiliation(s)
- Heewon Chung
- Department of Biomedical EngineeringWonkwang University College of MedicineIksan54538South Korea
| | - Hoon Ko
- Department of Biomedical EngineeringWonkwang University College of MedicineIksan54538South Korea
| | - Se Jeong Jeon
- Department of RadiologyWonkwang University College of MedicineIksan54538South Korea
| | - Kwon-Ha Yoon
- Department of RadiologyWonkwang University College of MedicineIksan54538South Korea
| | - Jinseok Lee
- Department of Biomedical EngineeringWonkwang University College of MedicineIksan54538South Korea
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Choi W, Oh JH, Riyahi S, Liu C, Jiang F, Chen W, White C, Rimner A, Mechalakos JG, Deasy JO, Lu W. Radiomics analysis of pulmonary nodules in low-dose CT for early detection of lung cancer. Med Phys 2018; 45:1537-1549. [PMID: 29457229 PMCID: PMC5903960 DOI: 10.1002/mp.12820] [Citation(s) in RCA: 85] [Impact Index Per Article: 14.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2017] [Revised: 02/05/2018] [Accepted: 02/07/2018] [Indexed: 01/13/2023] Open
Abstract
PURPOSE To develop a radiomics prediction model to improve pulmonary nodule (PN) classification in low-dose CT. To compare the model with the American College of Radiology (ACR) Lung CT Screening Reporting and Data System (Lung-RADS) for early detection of lung cancer. METHODS We examined a set of 72 PNs (31 benign and 41 malignant) from the Lung Image Database Consortium image collection (LIDC-IDRI). One hundred three CT radiomic features were extracted from each PN. Before the model building process, distinctive features were identified using a hierarchical clustering method. We then constructed a prediction model by using a support vector machine (SVM) classifier coupled with a least absolute shrinkage and selection operator (LASSO). A tenfold cross-validation (CV) was repeated ten times (10 × 10-fold CV) to evaluate the accuracy of the SVM-LASSO model. Finally, the best model from the 10 × 10-fold CV was further evaluated using 20 × 5- and 50 × 2-fold CVs. RESULTS The best SVM-LASSO model consisted of only two features: the bounding box anterior-posterior dimension (BB_AP) and the standard deviation of inverse difference moment (SD_IDM). The BB_AP measured the extension of a PN in the anterior-posterior direction and was highly correlated (r = 0.94) with the PN size. The SD_IDM was a texture feature that measured the directional variation of the local homogeneity feature IDM. Univariate analysis showed that both features were statistically significant and discriminative (P = 0.00013 and 0.000038, respectively). PNs with larger BB_AP or smaller SD_IDM were more likely malignant. The 10 × 10-fold CV of the best SVM model using the two features achieved an accuracy of 84.6% and 0.89 AUC. By comparison, Lung-RADS achieved an accuracy of 72.2% and 0.77 AUC using four features (size, type, calcification, and spiculation). The prediction improvement of SVM-LASSO comparing to Lung-RADS was statistically significant (McNemar's test P = 0.026). Lung-RADS misclassified 19 cases because it was mainly based on PN size, whereas the SVM-LASSO model correctly classified 10 of these cases by combining a size (BB_AP) feature and a texture (SD_IDM) feature. The performance of the SVM-LASSO model was stable when leaving more patients out with five- and twofold CVs (accuracy 84.1% and 81.6%, respectively). CONCLUSION We developed an SVM-LASSO model to predict malignancy of PNs with two CT radiomic features. We demonstrated that the model achieved an accuracy of 84.6%, which was 12.4% higher than Lung-RADS.
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Affiliation(s)
- Wookjin Choi
- Department of Medical
PhysicsMemorial Sloan Kettering Cancer CenterNew YorkNY10065USA
| | - Jung Hun Oh
- Department of Medical
PhysicsMemorial Sloan Kettering Cancer CenterNew YorkNY10065USA
| | - Sadegh Riyahi
- Department of Medical
PhysicsMemorial Sloan Kettering Cancer CenterNew YorkNY10065USA
| | - Chia‐Ju Liu
- Department of
RadiologyMemorial Sloan Kettering Cancer CenterNew YorkNY10065USA
| | - Feng Jiang
- Department of
PathologyUniversity of Maryland School of MedicineBaltimoreMD21201USA
| | - Wengen Chen
- Department of Diagnostic Radiology
and Nuclear MedicineUniversity of Maryland School of MedicineBaltimoreMD21201USA
| | - Charles White
- Department of Diagnostic Radiology
and Nuclear MedicineUniversity of Maryland School of MedicineBaltimoreMD21201USA
| | - Andreas Rimner
- Department of Radiation
OncologyMemorial Sloan Kettering Cancer CenterNew YorkNY10065USA
| | - James G. Mechalakos
- Department of Medical
PhysicsMemorial Sloan Kettering Cancer CenterNew YorkNY10065USA
| | - Joseph O. Deasy
- Department of Medical
PhysicsMemorial Sloan Kettering Cancer CenterNew YorkNY10065USA
| | - Wei Lu
- Department of Medical
PhysicsMemorial Sloan Kettering Cancer CenterNew YorkNY10065USA
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Zia ur Rehman M, Javaid M, Shah SIA, Gilani SO, Jamil M, Butt SI. An appraisal of nodules detection techniques for lung cancer in CT images. Biomed Signal Process Control 2018. [DOI: 10.1016/j.bspc.2017.11.017] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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Multistage segmentation model and SVM-ensemble for precise lung nodule detection. Int J Comput Assist Radiol Surg 2018; 13:1083-1095. [DOI: 10.1007/s11548-018-1715-9] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2017] [Accepted: 02/16/2018] [Indexed: 10/17/2022]
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