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Quanyang W, Yao H, Sicong W, Linlin Q, Zewei Z, Donghui H, Hongjia L, Shijun Z. Artificial intelligence in lung cancer screening: Detection, classification, prediction, and prognosis. Cancer Med 2024; 13:e7140. [PMID: 38581113 PMCID: PMC10997848 DOI: 10.1002/cam4.7140] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2023] [Revised: 03/15/2024] [Accepted: 03/16/2024] [Indexed: 04/08/2024] Open
Abstract
BACKGROUND The exceptional capabilities of artificial intelligence (AI) in extracting image information and processing complex models have led to its recognition across various medical fields. With the continuous evolution of AI technologies based on deep learning, particularly the advent of convolutional neural networks (CNNs), AI presents an expanded horizon of applications in lung cancer screening, including lung segmentation, nodule detection, false-positive reduction, nodule classification, and prognosis. METHODOLOGY This review initially analyzes the current status of AI technologies. It then explores the applications of AI in lung cancer screening, including lung segmentation, nodule detection, and classification, and assesses the potential of AI in enhancing the sensitivity of nodule detection and reducing false-positive rates. Finally, it addresses the challenges and future directions of AI in lung cancer screening. RESULTS AI holds substantial prospects in lung cancer screening. It demonstrates significant potential in improving nodule detection sensitivity, reducing false-positive rates, and classifying nodules, while also showing value in predicting nodule growth and pathological/genetic typing. CONCLUSIONS AI offers a promising supportive approach to lung cancer screening, presenting considerable potential in enhancing nodule detection sensitivity, reducing false-positive rates, and classifying nodules. However, the universality and interpretability of AI results need further enhancement. Future research should focus on the large-scale validation of new deep learning-based algorithms and multi-center studies to improve the efficacy of AI in lung cancer screening.
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Affiliation(s)
- Wu Quanyang
- Department of Diagnostic RadiologyNational Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Huang Yao
- Department of Diagnostic RadiologyNational Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Wang Sicong
- Magnetic Resonance Imaging ResearchGeneral Electric Healthcare (China)BeijingChina
| | - Qi Linlin
- Department of Diagnostic RadiologyNational Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Zhang Zewei
- PET‐CT CenterNational Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Hou Donghui
- Department of Diagnostic RadiologyNational Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Li Hongjia
- PET‐CT CenterNational Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Zhao Shijun
- Department of Diagnostic RadiologyNational Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
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Han L, Li F, Yu H, Xia K, Xin Q, Zou X. BiRPN-YOLOvX: A weighted bidirectional recursive feature pyramid algorithm for lung nodule detection. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2023; 31:301-317. [PMID: 36617767 DOI: 10.3233/xst-221310] [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: 06/17/2023]
Abstract
BACKGROUND Lung cancer has the second highest cancer mortality rate in the world today. Although lung cancer screening using CT images is a common way for early lung cancer detection, accurately detecting lung nodules remains a challenged issue in clinical practice. OBJECTIVE This study aims to develop a new weighted bidirectional recursive pyramid algorithm to address the problems of small size of lung nodules, large proportion of background region, and complex lung structures in lung nodule detection of CT images. METHODS First, the weighted bidirectional recursive feature pyramid network (BiPRN) is proposed, which can increase the ability of network model to extract feature information and achieve multi-scale fusion information. Second, a CBAM_CSPDarknet53 structure is developed to incorporate an attention mechanism as a feature extraction module, which can aggregate both spatial information and channel information of the feature map. Third, the weighted BiRPN and CBAM_CSPDarknet53 are applied to the YOLOvX model for lung nodule detection experiments, named BiRPN-YOLOvX, where YOLOvX represents different versions of YOLO. To verify the effectiveness of our weighted BiRPN and CBAM_ CSPDarknet53 algorithm, they are fused with different models of YOLOv3, YOLOv4 and YOLOv5, and extensive experiments are carried out using the publicly available lung nodule datasets LUNA16 and LIDC-IDRI. The training set of LUNA16 contains 949 images, and the validation and testing sets each contain 118 images. There are 1987, 248 and 248 images in LIDC-IDRI's training, validation and testing sets, respectively. RESULTS The sensitivity of lung nodule detection using BiRPN-YOLOv5 reaches 98.7% on LUNA16 and 96.2% on LIDC-IDRI, respectively. CONCLUSION This study demonstrates that the proposed new method has potential to help improve the sensitivity of lung nodule detection in future clinical practice.
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Affiliation(s)
- Liying Han
- School of Electronics and Information Engineering, Hebei University of Technology, Tianjin, China
| | - Fugai Li
- School of Electronics and Information Engineering, Hebei University of Technology, Tianjin, China
| | - Hengyong Yu
- Department of Electrical and Computer Engineering, University of Massachusetts Lowell, Lowell, MA, USA
| | - Kewen Xia
- School of Electronics and Information Engineering, Hebei University of Technology, Tianjin, China
| | - Qiyuan Xin
- School of Electronics and Information Engineering, Hebei University of Technology, Tianjin, China
| | - Xiaoyu Zou
- School of Electronics and Information Engineering, Hebei University of Technology, Tianjin, China
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Liang X, Kong Y, Shang H, Yang M, Lu W, Zeng Q, Zhang G, Ye X. Computed tomography findings, associated factors, and management of pulmonary nodules in 54,326 healthy individuals. J Cancer Res Ther 2022; 18:2041-2048. [PMID: 36647968 DOI: 10.4103/jcrt.jcrt_1586_22] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
Introduction To investigate the pulmonary nodules detected by low-dose computed tomography (LDCT), identified factors affecting the size and number of pulmonary nodules (single or multiple), and the pulmonary nodules diagnosed and management as lung cancer in healthy individuals. Methods A retrospective analysis was conducted on 54,326 healthy individuals who received chest LDCT screening. According to the results of screening, the detection rates of pulmonary nodules, grouped according to the size and number of pulmonary nodules (single or multiple), and the patients' gender, age, history of smoking, hypertension, and diabetes were statistically analyzed to determine the correlation between each factor and the characteristics of the nodules. The pulmonary nodules in healthy individuals diagnosed with lung cancer were managed with differently protocols. Results The detection rate of pulmonary nodules was 38.8% (21,055/54,326). The baseline demographic characteristics of patients with pulmonary nodules were: 58% male and 42% female patients, 25.7% smoking and 74.3% nonsmoking individuals, 40-60 years old accounted for 49%, 54.8% multiple nodules, and 45.2% single nodules, and ≤5-mm size accounted for 80.4%, 6-10 mm for 18.2%, and 11-30 mm for 1.4%. Multiple pulmonary nodules were more common in hypertensive patients. Diabetes is not an independent risk factor for several pulmonary nodules. Of all patients with lung nodules, 26 were diagnosed with lung cancer, accounting for 0.1% of all patients with pulmonary nodules, 0.6% with nodules ≥5 mm, and 2.2% with nodules ≥8 mm, respectively. Twenty-six patients with lung cancer were treated with surgical resection (57.7%), microwave ablation (MWA, 38.5%), and follow-up (3.8%). Conclusions LDCT was suitable for large-scale pulmonary nodules screening in healthy individuals, which was helpful for the early detection of suspicious lesions in the lung. In addition to surgical resection, MWA is an option for early lung cancer treatment.
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Affiliation(s)
- Xinyu Liang
- Department of Oncology, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, Shandong Key Laboratory of Rheumatic Disease and Translational Medicine, Shandong Lung Cancer Institute, No. 16766, Jingshi Road; Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong Province, China
| | - Yongmei Kong
- Shandong Second Provincial General Hospital, Jinan, Shandong Province, China
| | - Hui Shang
- Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong Province; Department of Radiology, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, No. 16766 Jingshi Road, Jinan, Shandong, China
| | - Mingxin Yang
- Department of Health Management, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, No. 16766, Jingshi Road, Jinan, Shandong Province, China
| | - Wenjing Lu
- Department of Oncology, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, Shandong Key Laboratory of Rheumatic Disease and Translational Medicine, Shandong Lung Cancer Institute, No. 16766, Jingshi Road; Shandong University of Traditional Chinese Medicine, Jinan, Shandong Province, China
| | - Qingshi Zeng
- Department of Radiology, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, No. 16766 Jingshi Road, Jinan, Shandong, China
| | - Guang Zhang
- Department of Health Management, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, No. 16766, Jingshi Road, Jinan, Shandong Province, China
| | - Xin Ye
- Department of Oncology, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, Shandong Key Laboratory of Rheumatic Disease and Translational Medicine, Shandong Lung Cancer Institute, No. 16766, Jingshi Road, Jinan, Shandong Province, China
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Deep fusion of gray level co-occurrence matrices for lung nodule classification. PLoS One 2022; 17:e0274516. [PMID: 36174073 PMCID: PMC9521911 DOI: 10.1371/journal.pone.0274516] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Accepted: 08/28/2022] [Indexed: 11/19/2022] Open
Abstract
Lung cancer is a serious threat to human health, with millions dying because of its late diagnosis. The computerized tomography (CT) scan of the chest is an efficient method for early detection and classification of lung nodules. The requirement for high accuracy in analyzing CT scan images is a significant challenge in detecting and classifying lung cancer. In this paper, a new deep fusion structure based on the long short-term memory (LSTM) has been introduced, which is applied to the texture features computed from lung nodules through new volumetric grey-level-co-occurrence-matrices (GLCMs), classifying the nodules into benign, malignant, and ambiguous. Also, an improved Otsu segmentation method combined with the water strider optimization algorithm (WSA) is proposed to detect the lung nodules. WSA-Otsu thresholding can overcome the fixed thresholds and time requirement restrictions in previous thresholding methods. Extended experiments are used to assess this fusion structure by considering 2D-GLCM based on 2D-slices and approximating the proposed 3D-GLCM computations based on volumetric 2.5D-GLCMs. The proposed methods are trained and assessed through the LIDC-IDRI dataset. The accuracy, sensitivity, and specificity obtained for 2D-GLCM fusion are 94.4%, 91.6%, and 95.8%, respectively. For 2.5D-GLCM fusion, the accuracy, sensitivity, and specificity are 97.33%, 96%, and 98%, respectively. For 3D-GLCM, the accuracy, sensitivity, and specificity of the proposed fusion structure reached 98.7%, 98%, and 99%, respectively, outperforming most state-of-the-art counterparts. The results and analysis also indicate that the WSA-Otsu method requires a shorter execution time and yields a more accurate thresholding process.
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Tomassini S, Falcionelli N, Sernani P, Burattini L, Dragoni AF. Lung nodule diagnosis and cancer histology classification from computed tomography data by convolutional neural networks: A survey. Comput Biol Med 2022; 146:105691. [PMID: 35691714 DOI: 10.1016/j.compbiomed.2022.105691] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2022] [Revised: 05/26/2022] [Accepted: 05/31/2022] [Indexed: 11/30/2022]
Abstract
Lung cancer is among the deadliest cancers. Besides lung nodule classification and diagnosis, developing non-invasive systems to classify lung cancer histological types/subtypes may help clinicians to make targeted treatment decisions timely, having a positive impact on patients' comfort and survival rate. As convolutional neural networks have proven to be responsible for the significant improvement of the accuracy in lung cancer diagnosis, with this survey we intend to: show the contribution of convolutional neural networks not only in identifying malignant lung nodules but also in classifying lung cancer histological types/subtypes directly from computed tomography data; point out the strengths and weaknesses of slice-based and scan-based approaches employing convolutional neural networks; and highlight the challenges and prospective solutions to successfully apply convolutional neural networks for such classification tasks. To this aim, we conducted a comprehensive analysis of relevant Scopus-indexed studies involved in lung nodule diagnosis and cancer histology classification up to January 2022, dividing the investigation in convolutional neural network-based approaches fed with planar or volumetric computed tomography data. Despite the application of convolutional neural networks in lung nodule diagnosis and cancer histology classification is a valid strategy, some challenges raised, mainly including the lack of publicly-accessible annotated data, together with the lack of reproducibility and clinical interpretability. We believe that this survey will be helpful for future studies involved in lung nodule diagnosis and cancer histology classification prior to lung biopsy by means of convolutional neural networks.
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Affiliation(s)
- Selene Tomassini
- Department of Information Engineering, Engineering Faculty, Università Politecnica delle Marche, Ancona, Italy.
| | - Nicola Falcionelli
- Department of Information Engineering, Engineering Faculty, Università Politecnica delle Marche, Ancona, Italy.
| | - Paolo Sernani
- Department of Information Engineering, Engineering Faculty, Università Politecnica delle Marche, Ancona, Italy.
| | - Laura Burattini
- Department of Information Engineering, Engineering Faculty, Università Politecnica delle Marche, Ancona, Italy.
| | - Aldo Franco Dragoni
- Department of Information Engineering, Engineering Faculty, Università Politecnica delle Marche, Ancona, Italy.
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Han Y, Qi H, Wang L, Chen C, Miao J, Xu H, Wang Z, Guo Z, Xu Q, Lin Q, Liu H, Lu J, Liang F, Feng W, Li H, Liu Y. Pulmonary nodules detection assistant platform: An effective computer aided system for early pulmonary nodules detection in physical examination. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 217:106680. [PMID: 35176595 DOI: 10.1016/j.cmpb.2022.106680] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Revised: 01/05/2022] [Accepted: 02/01/2022] [Indexed: 06/14/2023]
Abstract
BACKGROUND AND OBJECTIVE Early detection of the pulmonary nodule from physical examination low-dose computer tomography (LDCT) images is an effective measure to reduce the mortality rate of lung cancer. Although there are many computer aided diagnosis (CAD) methods used for detecting pulmonary nodules, there are few CAD systems for small pulmonary nodule detection with a large amount of physical examination LDCT images. METHODS In this work, we designed a CAD system called Pulmonary Nodules Detection Assistant Platform for early pulmonary nodules detection and classification based on the physical examination LDCT images. Based on the preprocessed physical examination CT images, the three-dimensional (3D) CNN-based model is presented to detect candidate pulmonary nodules and output detection results with quantitative parameters, the 3D ResNet is used to classify the detected nodules into intrapulmonary nodules and pleural nodules to reduce the physician workloads, and the Fully Connected Neural Network (FCNN) is used to classify ground-glass opacity (GGO) nodules and non-GGO nodules to help doctor pay more attention to those suspected early lung cancer nodules. RESULTS Experiments are performed on our 1000 samples of physical examinations (LNPE1000) with an average diameter of 5.3 mm and LUNA16 dataset with an average diameter of 8.31 mm, which show that the designed CAD system is automatic and efficient for detecting smaller and larger nodules from different datasets, especially for the detection of smaller nodules with diameter between 3 mm and 6 mm in physical examinations. The accuracy of pulmonary nodule detection reaches 0.879 with an average of 1 false positive per CT in LNPE1000 dataset, which is comparable to the experienced physicians. The classification accuracy reaches 0.911 between intrapulmonary and pleural nodules, and 0.950 between GGO and non-GGO nodules, respectively. CONCLUSION Experimental results show that the proposed pulmonary nodule detection model is robust for different datasets, which can successfully detect smaller and larger nodules in CT images obtained by physical examination. The interactive platform of the designed CAD system has been on trial in a hospital by combining with manual reading, which helps doctors analyze clinical data dynamically and improves the nodule detection efficiency in physical examination applications.
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Affiliation(s)
- Yu Han
- School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Honggang Qi
- School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Ling Wang
- School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Chen Chen
- Beijing Key Laboratory of Internet Culture and Digital Dissemination Research, Beijing Information Science and Technology University, Beijing, 100192, China
| | - Jun Miao
- Beijing Key Laboratory of Internet Culture and Digital Dissemination Research, Beijing Information Science and Technology University, Beijing, 100192, China
| | - Hongbo Xu
- School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Ziqi Wang
- School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Zhijun Guo
- Department of Radiology, Huabei Petroleum General Hospital, Heibei, 062550, China.
| | - Qian Xu
- Department of Radiology, Huabei Petroleum General Hospital, Heibei, 062550, China
| | - Qiang Lin
- Department of Oncology, Huabei Petroleum General Hospital, Heibei, 062550, China
| | - Haitao Liu
- Department of Respiratory Medicine, Huabei Petroleum General Hospital, Heibei, 062550, China
| | - Junying Lu
- Department of Radiology, Huabei Petroleum General Hospital, Heibei, 062550, China
| | - Fei Liang
- Department of Radiology, Huabei Petroleum General Hospital, Heibei, 062550, China
| | - Wenqiu Feng
- Department of Radiology, Huabei Petroleum General Hospital, Heibei, 062550, China
| | - Haiyan Li
- Department of Radiology, Huabei Petroleum General Hospital, Heibei, 062550, China
| | - Yan Liu
- School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing, 100049, China.
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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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Gong M, Chen S, Chen Q, Zeng Y, Zhang Y. Generative Adversarial Networks in Medical Image Processing. Curr Pharm Des 2021; 27:1856-1868. [PMID: 33238866 DOI: 10.2174/1381612826666201125110710] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2020] [Revised: 10/14/2020] [Accepted: 10/21/2020] [Indexed: 11/22/2022]
Abstract
BACKGROUND The emergence of generative adversarial networks (GANs) has provided new technology and framework for the application of medical images. Specifically, a GAN requires little to no labeled data to obtain high-quality data that can be generated through competition between the generator and discriminator networks. Therefore, GANs are rapidly proving to be a state-of-the-art foundation, achieving enhanced performances in various medical applications. METHODS In this article, we introduce the principles of GANs and their various variants, deep convolutional GAN, conditional GAN, Wasserstein GAN, Info-GAN, boundary equilibrium GAN, and cycle-GAN. RESULTS All various GANs have found success in medical imaging tasks, including medical image enhancement, segmentation, classification, reconstruction, and synthesis. Furthermore, we summarize the data processing methods and evaluation indicators. Finally, we note the limitations of existing methods and the existing challenges that need to be addressed in this field. CONCLUSION Although GANs are in the initial stage of development in medical image processing, it will have a great prospect in the future.
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Affiliation(s)
- Meiqin Gong
- West China Second University Hospital, Sichuan University, Chengdu 610041, China
| | - Siyu Chen
- School of Computer Science, Chengdu University of Information Technology, Chengdu 610225, China
| | - Qingyuan Chen
- School of Computer Science, Chengdu University of Information Technology, Chengdu 610225, China
| | - Yuanqi Zeng
- School of Computer Science, Chengdu University of Information Technology, Chengdu 610225, China
| | - Yongqing Zhang
- School of Computer Science, Chengdu University of Information Technology, Chengdu 610225, China
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Pedrosa J, Aresta G, Ferreira C, Atwal G, Phoulady HA, Chen X, Chen R, Li J, Wang L, Galdran A, Bouchachia H, Kaluva KC, Vaidhya K, Chunduru A, Tarai S, Nadimpalli SPP, Vaidya S, Kim I, Rassadin A, Tian Z, Sun Z, Jia Y, Men X, Ramos I, Cunha A, Campilho A. LNDb challenge on automatic lung cancer patient management. Med Image Anal 2021; 70:102027. [PMID: 33740739 DOI: 10.1016/j.media.2021.102027] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2020] [Revised: 01/18/2021] [Accepted: 02/26/2021] [Indexed: 12/21/2022]
Abstract
Lung cancer is the deadliest type of cancer worldwide and late detection is the major factor for the low survival rate of patients. Low dose computed tomography has been suggested as a potential screening tool but manual screening is costly and time-consuming. This has fuelled the development of automatic methods for the detection, segmentation and characterisation of pulmonary nodules. In spite of promising results, the application of automatic methods to clinical routine is not straightforward and only a limited number of studies have addressed the problem in a holistic way. With the goal of advancing the state of the art, the Lung Nodule Database (LNDb) Challenge on automatic lung cancer patient management was organized. The LNDb Challenge addressed lung nodule detection, segmentation and characterization as well as prediction of patient follow-up according to the 2017 Fleischner society pulmonary nodule guidelines. 294 CT scans were thus collected retrospectively at the Centro Hospitalar e Universitrio de So Joo in Porto, Portugal and each CT was annotated by at least one radiologist. Annotations comprised nodule centroids, segmentations and subjective characterization. 58 CTs and the corresponding annotations were withheld as a separate test set. A total of 947 users registered for the challenge and 11 successful submissions for at least one of the sub-challenges were received. For patient follow-up prediction, a maximum quadratic weighted Cohen's kappa of 0.580 was obtained. In terms of nodule detection, a sensitivity below 0.4 (and 0.7) at 1 false positive per scan was obtained for nodules identified by at least one (and two) radiologist(s). For nodule segmentation, a maximum Jaccard score of 0.567 was obtained, surpassing the interobserver variability. In terms of nodule texture characterization, a maximum quadratic weighted Cohen's kappa of 0.733 was obtained, with part solid nodules being particularly challenging to classify correctly. Detailed analysis of the proposed methods and the differences in performance allow to identify the major challenges remaining and future directions - data collection, augmentation/generation and evaluation of under-represented classes, the incorporation of scan-level information for better decision-making and the development of tools and challenges with clinical-oriented goals. The LNDb Challenge and associated data remain publicly available so that future methods can be tested and benchmarked, promoting the development of new algorithms in lung cancer medical image analysis and patient follow-up recommendation.
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Affiliation(s)
- João Pedrosa
- Institute for Systems and Computer Engineering, Technology and Science (INESC TEC), Porto, Portugal.
| | - Guilherme Aresta
- Institute for Systems and Computer Engineering, Technology and Science (INESC TEC), Porto, Portugal; Faculty of Engineering of the University of Porto (FEUP), Porto, Portugal
| | - Carlos Ferreira
- Institute for Systems and Computer Engineering, Technology and Science (INESC TEC), Porto, Portugal; Faculty of Engineering of the University of Porto (FEUP), Porto, Portugal
| | - Gurraj Atwal
- Department of Computer Science, California State University, Sacramento, USA
| | | | - Xiaoyu Chen
- Department of Computer Science, School of Informatics, Xiamen University, China
| | - Rongzhen Chen
- Department of Computer Science, School of Informatics, Xiamen University, China
| | - Jiaoliang Li
- Department of Computer Science, School of Informatics, Xiamen University, China
| | - Liansheng Wang
- Department of Computer Science, School of Informatics, Xiamen University, China
| | - Adrian Galdran
- Department of Computing and Informatics, Bournemouth University, UK
| | - Hamid Bouchachia
- Department of Computing and Informatics, Bournemouth University, UK
| | | | | | | | | | | | | | - Ildoo Kim
- Kakao Brain, Seongnam-si, South Korea
| | | | - Zhenhuan Tian
- Department of Thoracic Surgery, Peking Union Medical College Hospital, Peking Union Medical College, Beijing, China
| | | | - Yizhuan Jia
- Mediclouds Medical Technology, Beijing, China
| | - Xuejun Men
- Mediclouds Medical Technology, Beijing, China
| | - Isabel Ramos
- Institute for Systems and Computer Engineering, Technology and Science (INESC TEC), Porto, Portugal; Department of Radiology, Centro Hospitalar e Universitário de S. João, Porto, Portugal
| | - António Cunha
- Institute for Systems and Computer Engineering, Technology and Science (INESC TEC), Porto, Portugal; University of Trás-os-Montes e Alto Douro (UTAD), Vila Real, Portugal
| | - Aurélio Campilho
- Institute for Systems and Computer Engineering, Technology and Science (INESC TEC), Porto, Portugal; Faculty of Engineering of the University of Porto (FEUP), Porto, Portugal
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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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12
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Tan J, Jing L, Huo Y, Li L, Akin O, Tian Y. LGAN: Lung segmentation in CT scans using generative adversarial network. Comput Med Imaging Graph 2021; 87:101817. [PMID: 33278767 PMCID: PMC8477299 DOI: 10.1016/j.compmedimag.2020.101817] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2020] [Revised: 10/13/2020] [Accepted: 10/23/2020] [Indexed: 11/17/2022]
Abstract
Lung segmentation in Computerized Tomography (CT) images plays an important role in various lung disease diagnosis. Most of the current lung segmentation approaches are performed through a series of procedures with manually empirical parameter adjustments in each step. Pursuing an automatic segmentation method with fewer steps, we propose a novel deep learning Generative Adversarial Network (GAN)-based lung segmentation schema, which we denote as LGAN. The proposed schema can be generalized to different kinds of neural networks for lung segmentation in CT images. We evaluated the proposed LGAN schema on datasets including Lung Image Database Consortium image collection (LIDC-IDRI) and Quantitative Imaging Network (QIN) collection with two metrics: segmentation quality and shape similarity. Also, we compared our work with current state-of-the-art methods. The experimental results demonstrated that the proposed LGAN schema can be used as a promising tool for automatic lung segmentation due to its simplified procedure as well as its improved performance and efficiency.
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Affiliation(s)
- Jiaxing Tan
- The City University of New York, New York 10016, USA
| | - Longlong Jing
- The City University of New York, New York 10016, USA
| | - Yumei Huo
- The City University of New York, New York 10016, USA
| | - Lihong Li
- The City University of New York, New York 10016, USA
| | - Oguz Akin
- Memorial Sloan Kettering Cancer Center, New York 10065, USA
| | - Yingli Tian
- The City University of New York, New York 10016, USA.
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13
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ROI-based feature learning for efficient true positive prediction using convolutional neural network for lung cancer diagnosis. Neural Comput Appl 2020. [DOI: 10.1007/s00521-020-04787-w] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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14
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Shi Y, Gao Y, Zhang Y, Sun J, Mou X, Liang Z. Spectral CT Reconstruction via Low-Rank Representation and Region-Specific Texture Preserving Markov Random Field Regularization. IEEE TRANSACTIONS ON MEDICAL IMAGING 2020; 39:2996-3007. [PMID: 32217474 PMCID: PMC7529661 DOI: 10.1109/tmi.2020.2983414] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Photon-counting spectral computed tomography (CT) is capable of material characterization and can improve diagnostic performance over traditional clinical CT. However, it suffers from photon count starving for each individual energy channel which may cause severe artifacts in the reconstructed images. Furthermore, since the images in different energy channels describe the same object, there are high correlations among different channels. To make full use of the inter-channel correlations and minimize the count starving effect while maintaining clinically meaningful texture information, this paper combines a region-specific texture model with a low-rank correlation descriptor as an a priori regularization to explore a superior texture preserving Bayesian reconstruction of spectral CT. Specifically, the inter-channel correlations are characterized by the low-rank representation, and the inner-channel regional textures are modeled by a texture preserving Markov random field. In other words, this paper integrates the spectral and spatial information into a unified Bayesian reconstruction framework. The widely-used Split-Bregman algorithm is employed to minimize the objective function because of the non-differentiable property of the low-rank representation. To evaluate the tissue texture preserving performance of the proposed method for each channel, three references are built for comparison: one is the traditional CT image from energy integration detection. The second one is spectral images from dual-energy CT. The third one is individual channels images from custom-made photon-counting spectral CT. As expected, the proposed method produced promising results in terms of not only preserving texture features but also suppressing image noise in each channel, comparing to existing methods of total variation (TV), low-rank TV and tensor dictionary learning, by both visual inspection and quantitative indexes of root mean square error, peak signal to noise ratio, structural similarity and feature similarity.
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15
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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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16
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Gao Y, Liang Z, Zhang H, Yang J, Ferretti J, Bilfinger T, Yaddanapudi K, Schweitzer M, Bhattacharji P, Moore W. A Task-dependent Investigation on Dose and Texture in CT Image Reconstruction. IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES 2019; 4:441-449. [PMID: 33907724 DOI: 10.1109/trpms.2019.2957459] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
Localizing and characterizing clinically-significant lung nodules, a potential precursor to lung cancer, at the lowest achievable radiation dose is demanded to minimize the stochastic radiation effects from x-ray computed tomography (CT). A minimal dose level is heavily dependent on the image reconstruction algorithms and clinical task, in which the tissue texture always plays an important role. This study aims to investigate the dependence through a task-based evaluation at multiple dose levels and variable textures in reconstructions with prospective patient studies. 133 patients with a suspicious pulmonary nodule scheduled for biopsy were recruited and the data was acquired at120kVp with three different dose levels of 100, 40 and 20mAs. Three reconstruction algorithms were implemented: analytical filtered back-projection (FBP) with optimal noise filtering; statistical Markov random field (MRF) model with optimal Huber weighting (MRF-H) for piecewise smooth reconstruction; and tissue-specific texture model (MRF-T) for texture preserved statistical reconstruction. Experienced thoracic radiologists reviewed and scored all images at random, blind to the CT dose and reconstruction algorithms. The radiologists identified the nodules in each image including the 133 biopsy target nodules and 66 other non-target nodules. For target nodule characterization, only MRF-T at 40mAs showed no statistically significant difference from FBP at 100mAs. For localizing both the target nodules and the non-target nodules, some as small as 3mm, MRF-T at 40 and 20mAs levels showed no statistically significant difference from FBP at 100mAs, respectively. MRF-H and FBP at 40 and 20mAs levels performed statistically differently from FBP at 100mAs. This investigation concluded that (1) the textures in the MRF-T reconstructions improves both the tasks of localizing and characterizing nodules at low dose CT and (2) the task of characterizing nodules is more challenging than the task of localizing nodules and needs more dose or enhanced textures from reconstruction.
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Affiliation(s)
- Yongfeng Gao
- Department of Radiology, Stony Brook University, Stony Brook, NY 11794, USA
| | - Zhengrong Liang
- Departments of Radiology, Biomedical Engineering, Computer Science, and Electrical Engineering, Stony Brook University, Stony Brook, NY 11794, USA
| | - Hao Zhang
- Departments of Radiology and Biomedical Engineering, Stony Brook University, Stony Brook, NY 11794, USA and now with the Department of Radiation Oncology, Stanford University, Stanford, CA 94035, USA
| | - Jie Yang
- Department of Family, Population and Preventive Medicine, Stony Brook University, Stony Brook, NY 11794, USA
| | - John Ferretti
- Department of Radiology, Stony Brook University, Stony Brook, NY 11794, USA
| | - Thomas Bilfinger
- Department of Surgery, Stony Brook University, Stony Brook, NY 11794, USA)
| | | | - Mark Schweitzer
- Department of Radiology, Stony Brook University, Stony Brook, NY 11794, USA
| | - Priya Bhattacharji
- Department of Radiology, Stony Brook University, Stony Brook, NY 11794, USA, and now with the Department of Radiology, New York University, New York, NY 10016, USA
| | - William Moore
- Department of Radiology, Stony Brook University, Stony Brook, NY 11794, USA, and now with the Department of Radiology, New York University, New York, NY 10016, USA
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17
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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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18
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Gao Y, Shi Y, Cao W, Zhang S, Liang Z. Energy enhanced tissue texture in spectral computed tomography for lesion classification. Vis Comput Ind Biomed Art 2019; 2:16. [PMID: 32226923 PMCID: PMC7089716 DOI: 10.1186/s42492-019-0028-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2019] [Accepted: 10/16/2019] [Indexed: 12/30/2022] Open
Abstract
Tissue texture reflects the spatial distribution of contrasts of image voxel gray levels, i.e., the tissue heterogeneity, and has been recognized as important biomarkers in various clinical tasks. Spectral computed tomography (CT) is believed to be able to enrich tissue texture by providing different voxel contrast images using different X-ray energies. Therefore, this paper aims to address two related issues for clinical usage of spectral CT, especially the photon counting CT (PCCT): (1) texture enhancement by spectral CT image reconstruction, and (2) spectral energy enriched tissue texture for improved lesion classification. For issue (1), we recently proposed a tissue-specific texture prior in addition to low rank prior for the individual energy-channel low-count image reconstruction problems in PCCT under the Bayesian theory. Reconstruction results showed the proposed method outperforms existing methods of total variation (TV), low-rank TV and tensor dictionary learning in terms of not only preserving texture features but also suppressing image noise. For issue (2), this paper will investigate three models to incorporate the enriched texture by PCCT in accordance with three types of inputs: one is the spectral images, another is the co-occurrence matrices (CMs) extracted from the spectral images, and the third one is the Haralick features (HF) extracted from the CMs. Studies were performed on simulated photon counting data by introducing attenuation-energy response curve to the traditional CT images from energy integration detectors. Classification results showed the spectral CT enriched texture model can improve the area under the receiver operating characteristic curve (AUC) score by 7.3%, 0.42% and 3.0% for the spectral images, CMs and HFs respectively on the five-energy spectral data over the original single energy data only. The CM- and HF-inputs can achieve the best AUC of 0.934 and 0.927. This texture themed study shows the insight that incorporating clinical important prior information, e.g., tissue texture in this paper, into the medical imaging, such as the upstream image reconstruction, the downstream diagnosis, and so on, can benefit the clinical tasks.
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Affiliation(s)
- Yongfeng Gao
- 1Department of Radiology, Stony Brook University, Stony Brook, NY 11794 USA
| | - Yongyi Shi
- 1Department of Radiology, Stony Brook University, Stony Brook, NY 11794 USA.,2Institute of Image Processing and Pattern Recognition, Xi'an Jiaotong University, Xi'an, 710049 Shanxi China
| | - Weiguo Cao
- 1Department of Radiology, Stony Brook University, Stony Brook, NY 11794 USA
| | - Shu Zhang
- 1Department of Radiology, Stony Brook University, Stony Brook, NY 11794 USA
| | - Zhengrong Liang
- 3Departments of Radiology, Biomedical Engineering, Computer Science, and Electrical Engineering, Stony Brook University, Stony Brook, NY 11794 USA
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19
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Saba T, Sameh A, Khan F, Shad SA, Sharif M. Lung Nodule Detection based on Ensemble of Hand Crafted and Deep Features. J Med Syst 2019; 43:332. [DOI: 10.1007/s10916-019-1455-6] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2019] [Accepted: 09/10/2019] [Indexed: 12/27/2022]
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20
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Aresta G, Jacobs C, Araújo T, Cunha A, Ramos I, van Ginneken B, Campilho A. iW-Net: an automatic and minimalistic interactive lung nodule segmentation deep network. Sci Rep 2019; 9:11591. [PMID: 31406194 PMCID: PMC6690893 DOI: 10.1038/s41598-019-48004-8] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2019] [Accepted: 06/28/2019] [Indexed: 11/09/2022] Open
Abstract
We propose iW-Net, a deep learning model that allows for both automatic and interactive segmentation of lung nodules in computed tomography images. iW-Net is composed of two blocks: the first one provides an automatic segmentation and the second one allows to correct it by analyzing 2 points introduced by the user in the nodule’s boundary. For this purpose, a physics inspired weight map that takes the user input into account is proposed, which is used both as a feature map and in the system’s loss function. Our approach is extensively evaluated on the public LIDC-IDRI dataset, where we achieve a state-of-the-art performance of 0.55 intersection over union vs the 0.59 inter-observer agreement. Also, we show that iW-Net allows to correct the segmentation of small nodules, essential for proper patient referral decision, as well as improve the segmentation of the challenging non-solid nodules and thus may be an important tool for increasing the early diagnosis of lung cancer.
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Affiliation(s)
- Guilherme Aresta
- INESC TEC - Institute for Systems and Computer Engineering, Technology and Science, Rua Doutor Roberto Frias, 4200-465, Porto, Portugal. .,Faculty of Engineering of University of Porto, Rua Doutor Roberto Frias, 4200-465, Porto, Portugal.
| | - Colin Jacobs
- Radboud University Medical Center, 6525, Nijmegen, The Netherlands
| | - Teresa Araújo
- INESC TEC - Institute for Systems and Computer Engineering, Technology and Science, Rua Doutor Roberto Frias, 4200-465, Porto, Portugal.,Faculty of Engineering of University of Porto, Rua Doutor Roberto Frias, 4200-465, Porto, Portugal
| | - António Cunha
- INESC TEC - Institute for Systems and Computer Engineering, Technology and Science, Rua Doutor Roberto Frias, 4200-465, Porto, Portugal.,University of Trás-os-Montes e Alto Douro, Quinta de Prados, 5001-801, Vila Real, Portugal
| | - Isabel Ramos
- Faculty of Medicine of University of Porto, Alameda Prof. Hernâni Monteiro, 4200-319, Porto, Portugal
| | | | - Aurélio Campilho
- INESC TEC - Institute for Systems and Computer Engineering, Technology and Science, Rua Doutor Roberto Frias, 4200-465, Porto, Portugal.,Faculty of Engineering of University of Porto, Rua Doutor Roberto Frias, 4200-465, Porto, Portugal
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21
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Gao Y, Liang Z, Moore WH, Zhang H, Pomeroy MJ, Ferretti JA, Bilfinger TV, Ma J, Lu H. A Feasibility Study of Extracting Tissue Textures From a Previous Full-Dose CT Database as Prior Knowledge for Bayesian Reconstruction of Current Low-Dose CT Images. IEEE TRANSACTIONS ON MEDICAL IMAGING 2019; 38:1981-1992. [PMID: 30605098 PMCID: PMC6610633 DOI: 10.1109/tmi.2018.2890788] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Markov random field (MRF) has been widely used to incorporate a priori knowledge as penalty or regularizer to preserve edge sharpness while smoothing the region enclosed by the edge for pieces-wise smooth image reconstruction. In our earlier study, we proposed a type of MRF reconstruction method for low-dose CT (LdCT) scans using tissue-specific textures extracted from the same patient's previous full-dose CT (FdCT) scans as prior knowledge. It showed advantages in clinical applications. This paper aims to remove the constraint of using previous data of the same patient. We investigated the feasibility of extracting the tissue-specific MRF textures from an FdCT database to reconstruct a LdCT image of another patient. This feasibility study was carried out by experiments designed as follows. We constructed a tissue-specific MRF-texture database from 3990 FdCT scan slices of 133 patients who were scheduled for lung nodule biopsy. Each patient had one FdCT scan (120 kVp/100 mAs) and one LdCT scan (120 kVp/20 mAs) prior to biopsy procedure. When reconstructing the LdCT image of one patient among the 133 patients, we ranked the closeness of the MRF-textures from the other 132 patients saved in the database and used them as the a prior knowledge. Then, we evaluated the reconstructed image quality using Haralick texture measures. For any patient within our database, we found more than eighteen patients' FdCT MRF texures can be used without noticeably changing the Haralick texture measures on the lung nodules (to be biopsied). These experimental outcomes indicate it is promising that a sizable FdCT texture database could be used to enhance Bayesian reconstructions of any incoming LdCT scans.
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Affiliation(s)
- Yongfeng Gao
- Department of Radiology, State University of New York at Stony Brook, Stony Brook, NY 11974 USA
| | - Zhengrong Liang
- Departments of Radiology, Electrical and Computer Engineering, Computer Science and Biomedical Engineering, State University of New York at Stony Brook, Stony Brook, NY 11794, USA ()
| | - William H. Moore
- Department of Radiology, State University of New York at Stony Brook, Stony Brook, NY 11794, USA, and now is with the Department of Radiology, New York University, New York, NY 10016, USA
| | - Hao Zhang
- Department of Radiation Oncology, Stanford University, Stanford, CA 94035, USA
| | - Marc J. Pomeroy
- Departments of Radiology and Biomedical Engineering, State University of New York at Stony Brook, Stony Brook, NY 11794, USA
| | - John A. Ferretti
- Department of Radiology, State University of New York at Stony Brook, Stony Brook, NY 11794, USA
| | - Thomas V. Bilfinger
- Department of Surgery, State University of New York at Stony Brook, Stony Brook, NY 11794, USA
| | - Jianhua Ma
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China
| | - Hongbing Lu
- Department of Biomedical Engineering, Fourth Military Medical University, Xi’an 710032, China
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22
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Kar S, Das Sharma K, Maitra M. Adaptive weighted aggregation in Group Improvised Harmony Search for lung nodule classification. J EXP THEOR ARTIF IN 2019. [DOI: 10.1080/0952813x.2019.1647561] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
Affiliation(s)
- Subhajit Kar
- Department of Electrical Engineering, Future Institute of Engineering and Management, Kolkata, India
| | | | - Madhubanti Maitra
- Department of Electrical Engineering, Jadavpur University, Kolkata, India
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23
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Manickavasagam R, Selvan S. Automatic Detection and Classification of Lung Nodules in CT Image Using Optimized Neuro Fuzzy Classifier with Cuckoo Search Algorithm. J Med Syst 2019; 43:77. [DOI: 10.1007/s10916-019-1177-9] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2018] [Accepted: 01/21/2019] [Indexed: 12/19/2022]
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24
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Malathi M, Sinthia P, Jalaldeen K. Active Contour Based Segmentation and Classification for Pleura Diseases Based on Otsu’s Thresholding and Support Vector Machine (SVM). Asian Pac J Cancer Prev 2019; 20:167-173. [PMID: 30678428 PMCID: PMC6485560 DOI: 10.31557/apjcp.2019.20.1.167] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2018] [Accepted: 01/02/2019] [Indexed: 11/29/2022] Open
Abstract
Objective: Generally, lung cancer is the abnormal growth of cells that originates in one or both lungs. Finding the pulmonary nodule helps in the diagnosis of lung cancer in early stage and also increase the lifetime of the individual. Accurate segmentation of normal and abnormal portion in segmentation is challenging task in computer-aided diagnostics. Methods: The article proposes an innovative method to spot the cancer portion using Otsu’s segmentation algorithm. It is followed by a Support Vector Machine (SVM) classifier to classify the abnormal portion of the lung image. Results: The suggested methods use the Otsu’s thresholding and active contour based segmentation techniques to locate the affected lung nodule of CT images. The segmentation is followed by an SVM classifier in order to categorize the affected portion is normal or abnormal. The proposed method is suitable to provide good and accurate segmentation and classification results for complex images. Conclusion: The comparative analysis between the two segmentation methods along with SVM classifier was performed. A classification process based on active contour and SVM techniques provides better than Otsu’s segmentation for complex lung images.
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Affiliation(s)
- M Malathi
- Department of Electronics and Instrumentation, Saveetha Engineering College, Chennai, India.
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25
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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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Automatic nodule detection for lung cancer in CT images: A review. Comput Biol Med 2018; 103:287-300. [PMID: 30415174 DOI: 10.1016/j.compbiomed.2018.10.033] [Citation(s) in RCA: 57] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2018] [Revised: 10/29/2018] [Accepted: 10/29/2018] [Indexed: 12/18/2022]
Abstract
Automatic lung nodule detection has great significance for treating lung cancer and increasing patient survival. This work summarizes a critical review of recent techniques for automatic lung nodule detection in computed tomography images. This review indicates the current tendency and obtained progress as well as future challenges in this field. This research covered the databases including Web of Science, PubMed, and the Press, including IEEE Xplore and Science Direct, up to May 2018. Each part of the paper is summarized carefully in terms of the method and validation results for better comparison. Based on the results, some techniques show better performance for lung nodule detection. However, researchers should pay attention to the existing challenges, such as high sensitivity with a low false positive rate, large and different patient databases, developing or optimizing the detection technique of various types of lung nodules with different sizes, shapes, textures and locations, combining electronic medical records and picture archiving and communication systems, building efficient feature sets for better classification and promoting the cooperation and communication between academic institutions and medical organizations. We believe that automatic computer-aided detection systems will be developed with strong robustness, high efficiency and security assurance. This review will be helpful for professional researchers and radiologists to further learn about the latest techniques in computer-aided detection systems.
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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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Dandıl E. A Computer-Aided Pipeline for Automatic Lung Cancer Classification on Computed Tomography Scans. JOURNAL OF HEALTHCARE ENGINEERING 2018; 2018:9409267. [PMID: 30515286 PMCID: PMC6236771 DOI: 10.1155/2018/9409267] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/28/2018] [Revised: 09/24/2018] [Accepted: 10/08/2018] [Indexed: 11/17/2022]
Abstract
Lung cancer is one of the most common cancer types. For the survival of the patient, early detection of lung cancer with the best treatment method is crucial. In this study, we propose a novel computer-aided pipeline on computed tomography (CT) scans for early diagnosis of lung cancer thanks to the classification of benign and malignant nodules. The proposed pipeline is composed of four stages. In preprocessing steps, CT images are enhanced, and lung volumes are extracted from the image with the help of a novel method called lung volume extraction method (LUVEM). The significance of the proposed pipeline is using LUVEM for extracting lung region. In nodule detection stage, candidate nodules are determined according to the circular Hough transform- (CHT-) based method. Then, lung nodules are segmented with self-organizing maps (SOM). In feature computation stage, intensity, shape, texture, energy, and combined features are used for feature extraction, and principal component analysis (PCA) is used for feature reduction step. In the final stage, probabilistic neural network (PNN) classifies benign and malign nodules. According to the experiments performed on our dataset, the proposed pipeline system can classify benign and malign nodules with 95.91% accuracy, 97.42% sensitivity, and 94.24% specificity. Even in cases of small-sized nodules (3-10 mm), the proposed system can determine the nodule type with 94.68% accuracy.
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Affiliation(s)
- Emre Dandıl
- Department of Computer Engineering, Faculty of Engineering, Bilecik Seyh Edebali University, Gulumbe Campus, 11210 Bilecik, Turkey
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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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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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Woźniak M, Połap D, Capizzi G, Sciuto GL, Kośmider L, Frankiewicz K. Small lung nodules detection based on local variance analysis and probabilistic neural network. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2018; 161:173-180. [PMID: 29852959 DOI: 10.1016/j.cmpb.2018.04.025] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/14/2018] [Revised: 04/10/2018] [Accepted: 04/26/2018] [Indexed: 06/08/2023]
Abstract
BACKGROUND AND OBJECTIVE In medical examinations doctors use various techniques in order to provide to the patients an accurate analysis of their actual state of health. One of the commonly used methodologies is the x-ray screening. This examination very often help to diagnose some diseases of chest organs. The most frequent cause of wrong diagnosis lie in the radiologist's difficulty in interpreting the presence of lungs carcinoma in chest X-ray. In such circumstances, an automated approach could be highly advantageous as it provides important help in medical diagnosis. METHODS In this paper we propose a new classification method of the lung carcinomas. This method start with the localization and extraction of the lung nodules by computing, for each pixel of the original image, the local variance obtaining an output image (variance image) with the same size of the original image. In the variance image we find the local maxima and then by using the locations of these maxima in the original image we found the contours of the possible nodules in lung tissues. However after this segmentation stage we find many false nodules. Therefore to discriminate the true ones we use a probabilistic neural network as classifier. RESULTS The performance of our approach is 92% of correct classifications, while the sensitivity is 95% and the specificity is 89.7%. The misclassification errors are due to the fact that network confuses false nodules with the true ones (6%) and true nodules with the false ones (2%). CONCLUSIONS Several researchers have proposed automated algorithms to detect and classify pulmonary nodules but these methods fail to detect low-contrast nodules and have a high computational complexity, in contrast our method is relatively simple but at the same time provides good results and can detect low-contrast nodules. Furthermore, in this paper is presented a new algorithm for training the PNN neural networks that allows to obtain PNNs with many fewer neurons compared to the neural networks obtained by using the training algorithms present in the literature. So considerably lowering the computational burden of the trained network and at same time keeping the same performances.
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Affiliation(s)
- Marcin Woźniak
- Institute of Mathematics, Silesian University of Technology, Kaszubska 23, Gliwice 44-100, Poland; Department of Electric, Electronic and Informatics Engineering, University of Catania, Viale A. Doria 6, Catania 95125, Italy.
| | - Dawid Połap
- Institute of Mathematics, Silesian University of Technology, Kaszubska 23, Gliwice 44-100, Poland; Department of Electric, Electronic and Informatics Engineering, University of Catania, Viale A. Doria 6, Catania 95125, Italy.
| | - Giacomo Capizzi
- Institute of Mathematics, Silesian University of Technology, Kaszubska 23, Gliwice 44-100, Poland; Department of Electric, Electronic and Informatics Engineering, University of Catania, Viale A. Doria 6, Catania 95125, Italy.
| | - Grazia Lo Sciuto
- Department of Electric, Electronic and Informatics Engineering, University of Catania, Viale A. Doria 6, Catania 95125, Italy.
| | - Leon Kośmider
- School of Pharmacy with the Division of Laboratory Medicine in Sosnowiec, Department of General and Analytical Chemistry Medical University of Silesia, Jagiellońska 4, Sosnowiec 41-200, Poland.
| | - Katarzyna Frankiewicz
- Specialist Hospital Sz. Starkiewicz in Da̧browa Górnicza, Zagłȩbiowskie Oncology Centre, Szpitalna 13, Da̧browa Górnicza 41-300, Poland.
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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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Wei G, Ma H, Qian W, Han F, Jiang H, Qi S, Qiu M. Lung nodule classification using local kernel regression models with out-of-sample extension. Biomed Signal Process Control 2018. [DOI: 10.1016/j.bspc.2017.08.026] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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Gupta A, Saar T, Martens O, Moullec YL. Automatic detection of multisize pulmonary nodules in CT images: Large-scale validation of the false-positive reduction step. Med Phys 2018; 45:1135-1149. [DOI: 10.1002/mp.12746] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2016] [Revised: 11/07/2017] [Accepted: 12/14/2017] [Indexed: 11/08/2022] Open
Affiliation(s)
- Anindya Gupta
- Thomas Johann Seebeck Department of Electronics; Tallinn University of Technology; Tallinn 19086 Estonia
| | - Tonis Saar
- Eliko Tehnoloogia Arenduskeskus OÜ; Tallinn 12618 and OÜ Tallinn 10143 Estonia
| | - Olev Martens
- Thomas Johann Seebeck Department of Electronics; Tallinn University of Technology; Tallinn 19086 Estonia
| | - Yannick Le Moullec
- Thomas Johann Seebeck Department of Electronics; Tallinn University of Technology; Tallinn 19086 Estonia
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A unified methodology based on sparse field level sets and boosting algorithms for false positives reduction in lung nodules detection. Int J Comput Assist Radiol Surg 2017; 13:397-409. [DOI: 10.1007/s11548-017-1656-8] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2017] [Accepted: 07/31/2017] [Indexed: 01/15/2023]
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Jiang H, Ma H, Qian W, Gao M, Li Y. An Automatic Detection System of Lung Nodule Based on Multigroup Patch-Based Deep Learning Network. IEEE J Biomed Health Inform 2017; 22:1227-1237. [PMID: 28715341 DOI: 10.1109/jbhi.2017.2725903] [Citation(s) in RCA: 93] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
High-efficiency lung nodule detection dramatically contributes to the risk assessment of lung cancer. It is a significant and challenging task to quickly locate the exact positions of lung nodules. Extensive work has been done by researchers around this domain for approximately two decades. However, previous computer-aided detection (CADe) schemes are mostly intricate and time-consuming since they may require more image processing modules, such as the computed tomography image transformation, the lung nodule segmentation, and the feature extraction, to construct a whole CADe system. It is difficult for these schemes to process and analyze enormous data when the medical images continue to increase. Besides, some state of the art deep learning schemes may be strict in the standard of database. This study proposes an effective lung nodule detection scheme based on multigroup patches cut out from the lung images, which are enhanced by the Frangi filter. Through combining two groups of images, a four-channel convolution neural networks model is designed to learn the knowledge of radiologists for detecting nodules of four levels. This CADe scheme can acquire the sensitivity of 80.06% with 4.7 false positives per scan and the sensitivity of 94% with 15.1 false positives per scan. The results demonstrate that the multigroup patch-based learning system is efficient to improve the performance of lung nodule detection and greatly reduce the false positives under a huge amount of image data.
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Jose D, Chithara AN, Nirmal Kumar P, Kareemulla H. Automatic Detection of Lung Cancer Nodules in Computerized Tomography Images. NATIONAL ACADEMY SCIENCE LETTERS 2017. [DOI: 10.1007/s40009-017-0549-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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Takahashi R, Kajikawa Y. Computer-aided diagnosis: A survey with bibliometric analysis. Int J Med Inform 2017; 101:58-67. [DOI: 10.1016/j.ijmedinf.2017.02.004] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2016] [Revised: 01/28/2017] [Accepted: 02/04/2017] [Indexed: 12/18/2022]
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Zhang H, Han H, Liang Z, Hu Y, Liu Y, Moore W, Ma J, Lu H. Extracting Information From Previous Full-Dose CT Scan for Knowledge-Based Bayesian Reconstruction of Current Low-Dose CT Images. IEEE TRANSACTIONS ON MEDICAL IMAGING 2016; 35:860-870. [PMID: 26561284 PMCID: PMC4783190 DOI: 10.1109/tmi.2015.2498148] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2023]
Abstract
Markov random field (MRF) model has been widely employed in edge-preserving regional noise smoothing penalty to reconstruct piece-wise smooth images in the presence of noise, such as in low-dose computed tomography (LdCT). While it preserves edge sharpness, its regional smoothing may sacrifice tissue image textures, which have been recognized as useful imaging biomarkers, and thus it may compromise clinical tasks such as differentiating malignant vs. benign lesions, e.g., lung nodules or colon polyps. This study aims to shift the edge-preserving regional noise smoothing paradigm to texture-preserving framework for LdCT image reconstruction while retaining the advantage of MRF's neighborhood system on edge preservation. Specifically, we adapted the MRF model to incorporate the image textures of muscle, fat, bone, lung, etc. from previous full-dose CT (FdCT) scan as a priori knowledge for texture-preserving Bayesian reconstruction of current LdCT images. To show the feasibility of the proposed reconstruction framework, experiments using clinical patient scans were conducted. The experimental outcomes showed a dramatic gain by the a priori knowledge for LdCT image reconstruction using the commonly-used Haralick texture measures. Thus, it is conjectured that the texture-preserving LdCT reconstruction has advantages over the edge-preserving regional smoothing paradigm for texture-specific clinical applications.
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Affiliation(s)
| | | | | | - Yifan Hu
- Dept. of Radiology, State University of New York at Stony Brook, NY 11794 USA
| | - Yan Liu
- Dept. of Radiology, State University of New York at Stony Brook, NY 11794 USA
| | - William Moore
- Dept. of Radiology, State University of New York at Stony Brook, NY 11794 USA
| | - Jianhua Ma
- Dept. of Biomedical Engineering, Southern Medical University, Guangdong 510515, China
| | - Hongbing Lu
- Dept. of Biomedical Engineering, Fourth Military Medical University, Shaanxi 710032, China
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Computer-aided detection (CADe) and diagnosis (CADx) system for lung cancer with likelihood of malignancy. Biomed Eng Online 2016; 15:2. [PMID: 26759159 PMCID: PMC5002110 DOI: 10.1186/s12938-015-0120-7] [Citation(s) in RCA: 102] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2015] [Accepted: 12/22/2015] [Indexed: 01/04/2023] Open
Abstract
Background CADe and CADx systems for the detection and diagnosis of lung cancer have been important areas of research in recent decades. However, these areas are being worked on separately. CADe systems do not present the radiological characteristics of tumors, and CADx systems do not detect nodules and do not have good levels of automation. As a result, these systems are not yet widely used in clinical settings. Methods The purpose of this article is to develop a new system for detection and diagnosis of pulmonary nodules on CT images, grouping them into a single system for the identification and characterization of the nodules to improve the level of automation. The article also presents as contributions: the use of Watershed and Histogram of oriented Gradients (HOG) techniques for distinguishing the possible nodules from other structures and feature extraction for pulmonary nodules, respectively. For the diagnosis, it is based on the likelihood of malignancy allowing more aid in the decision making by the radiologists. A rule-based classifier and Support Vector Machine (SVM) have been used to eliminate false positives. Results The database used in this research consisted of 420 cases obtained randomly from LIDC-IDRI. The segmentation method achieved an accuracy of 97 % and the detection system showed a sensitivity of 94.4 % with 7.04 false positives per case. Different types of nodules (isolated, juxtapleural, juxtavascular and ground-glass) with diameters between 3 mm and 30 mm have been detected. For the diagnosis of malignancy our system presented ROC curves with areas of: 0.91 for nodules highly unlikely of being malignant, 0.80 for nodules moderately unlikely of being malignant, 0.72 for nodules with indeterminate malignancy, 0.67 for nodules moderately suspicious of being malignant and 0.83 for nodules highly suspicious of being malignant. Conclusions From our preliminary results, we believe that our system is promising for clinical applications assisting radiologists in the detection and diagnosis of lung cancer.
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Saien S, Hamid Pilevar A, Abrishami Moghaddam H. Refinement of lung nodule candidates based on local geometric shape analysis and Laplacian of Gaussian kernels. Comput Biol Med 2014; 54:188-98. [PMID: 25303113 DOI: 10.1016/j.compbiomed.2014.09.010] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2014] [Revised: 09/16/2014] [Accepted: 09/17/2014] [Indexed: 10/24/2022]
Abstract
This work is focused on application of a new technique in the first steps of computer-aided detection (CAD) of lung nodules. The scheme includes segmenting the lung volume and detecting most of the nodules with a low number of false positive (FP) objects. The juxtapleural nodules were properly included and the airways excluded in the lung segmentation. Among the suspicious regions obtained from the multiscale dot enhancement filter, those containing the center of nodule candidates, were determined. These center points were achieved from a 3D blob detector based on Laplacian of Gaussian kernels. Then the volumetric shape index (SI) that encodes the 3D local shape information was calculated for voxels in the determined regions. The performance of the scheme was evaluated by using 42 CT images from the Lung Image Database Consortium (LIDC). The results show that the average number of FPs reaches to 38.8 per scan with the sensitivity of 95.9% in the initial detections. The scheme is adaptable to detect nodules with wide variations in size, shape, intensity and location. Comparison of results with previously reported ones indicates that the proposed scheme can be satisfactory applied for initial detection of lung nodules in the chest CT images.
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Affiliation(s)
- Soudeh Saien
- Department of Computing Engineering, Bu-Ali Sina University, Hamedan, Iran.
| | | | - Hamid Abrishami Moghaddam
- Department of Biomedical Engineering, Faculty of Electrical Engineering, K.N. Toosi University of Technology, Tehran, Iran.
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