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Comparing and combining algorithms for computer-aided detection of pulmonary nodules in computed tomography scans: The ANODE09 study

Bram van Ginneken, Samuel G. Armato, Bartjan de Hoop, Saskia van Amelsvoort-van de Vorst, Thomas Duindam, Meindert Niemeijer, Keelin Murphy, Arnold Schilham, Alessandra Retico, Maria Evelina Fantacci, Niccolò Camarlinghi, Francesco Bagagli (+12 others)
2010 Medical Image Analysis  
Numerous publications and commercial systems are available that deal with automatic detection of pulmonary nodules in thoracic computed tomography scans, but a comparative study where many systems are  ...  This paper introduces ANODE09 ( http://, a database of 55 scans from a lung cancer screening program and a web-based framework for objective evaluation of nodule detection algorithms  ...  We would like to thank the organizers of SPIE Medical Imaging for allowing us to organize a special session devoted to ANODE09 at the SPIE Medical Imaging The Gifu team would like to thank Dr.  ... 
doi:10.1016/ pmid:20573538 fatcat:mj2cw6si4ze25kwb4qycnkykie

Analysis of various classification techniques for computer aided detection system of pulmonary nodules in CT

Barath Narayanan Narayanan, Russell C. Hardie, Temesguen M. Kebede
2016 2016 IEEE National Aerospace and Electronics Conference (NAECON) and Ohio Innovation Summit (OIS)  
Nodules are round or oval-shaped growth present in the lung. Computed Tomography (CT) scans are used by radiologists to detect such nodules.  ...  Computer Aided Detection (CAD) of such nodules would aid in providing a second opinion to the radiologists and would be of valuable help in lung cancer screening.  ...  Thus, Computer Aided Detection (CAD) for automatically identifying such pulmonary nodules on CT is very essential and would be of great help for lung cancer screening.  ... 
doi:10.1109/naecon.2016.7856779 fatcat:vjjnivmhf5dyhhjuckic7ktv5a

Deep Learning on Point Clouds for False Positive Reduction at Nodule Detection in Chest CT Scans [article]

Ivan Drokin, Elena Ericheva
2020 arXiv   pre-print
This paper focuses on a novel approach for false-positive reduction (FPR) of nodule candidates in Computer-aided detection (CADe) systems following the suspicious lesions detection stage.  ...  In some cases, the volume of the nodule candidate can be much smaller than the surrounding context, for example, in the case of subpleural localization of the nodule.  ...  CNNs framework for automatic pulmonary nodule detection in CT scans.  ... 
arXiv:2005.03654v2 fatcat:l4hqcvrhprgebimom7yoywetii

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

Wenming Cao, Rui Wu, Guitao Cao, Zhihai He
2020 IEEE Access  
Low-Dose Computed Tomography (LDCT) screening has been proved as a practical technique for improving the accuracy of pulmonary nodule detection and classification at early cancer diagnosis, which contributes  ...  Therefore, with the explosive growth of CT data, it is of great clinical significance to exploit an effective Computer-Aided Diagnosis (CAD) system for radiologists on automatic nodule analysis.  ...  This paper aims to provide a detailed overview of CAD systems for pulmonary nodule detection and classification, which can be used as a study guide for researchers.  ... 
doi:10.1109/access.2020.3018666 fatcat:efatgjz7srg5vjfqrx75ttyzbu

Computer-aided detection system for lung cancer in computed tomography scans: Review and future prospects

Macedo Firmino, Antônio H Morais, Roberto M Mendoça, Marcel R Dantas, Helio R Hekis, Ricardo Valentim
2014 BioMedical Engineering OnLine  
The goal of this paper is to present a critical review of major Computer-Aided Detection systems (CADe) for lung cancer in order to identify challenges for future research.  ...  However, no significant improvement was observed in sensitivity, number of false positives, level of automation and ability to detect different types and shapes of nodules in the studied period.  ...  Acknowledgements The authors acknowledge the National Cancer Institute and the Foundation for the National Institutes of Health and their critical role in the creation of the free publicly available LIDC  ... 
doi:10.1186/1475-925x-13-41 pmid:24713067 pmcid:PMC3995505 fatcat:4ku7khqrtjadhdixa42a3oxfsi

The Performance of Deep Learning Algorithms on Automatic Pulmonary Nodule Detection and Classification Tested on Different Datasets That Are Not Derived from LIDC-IDRI: A Systematic Review

Dana Li, Bolette Mikela Vilmun, Jonathan Frederik Carlsen, Elisabeth Albrecht-Beste, Carsten Ammitzbøl Lauridsen, Michael Bachmann Nielsen, Kristoffer Lindskov Hansen
2019 Diagnostics  
The aim of this study was to systematically review the performance of deep learning technology in detecting and classifying pulmonary nodules on computed tomography (CT) scans that were not from the Lung  ...  Three studies explored the performance of pulmonary nodule detection only, 16 studies explored the performance of pulmonary nodule classification only, and 7 studies had reports of both pulmonary nodule  ...  To aid radiologists in more accurate and time-efficient detection and diagnosis of pulmonary nodules, several computer-aided diagnosis and detection schemes have been developed [8] [9] [10] ; the best  ... 
doi:10.3390/diagnostics9040207 pmid:31795409 pmcid:PMC6963966 fatcat:q4pqvzx3yzdwjdwv3rp4dnbpwa

Large scale validation of the M5L lung CAD on heterogeneous CT datasets

E. Lopez Torres, E. Fiorina, F. Pennazio, C. Peroni, M. Saletta, N. Camarlinghi, M. E. Fantacci, P. Cerello
2015 Medical Physics (Lancaster)  
Purpose: M5L, a fully automated computer-aided detection (CAD) system for the detection and segmentation of lung nodules in thoracic computed tomography (CT), is presented and validated on several image  ...  The main aim of the present study was accomplished: M5L results do not deteriorate when increasing the dataset size, making it a candidate for supporting radiologists on large scale screenings and clinical  ...  Since lung cancer is most frequently detectable as noncalcified pulmonary nodules, computed tomography (CT) is the most appropriate imaging modality for its early detection. 3 The concept of screening  ... 
doi:10.1118/1.4907970 pmid:25832038 pmcid:PMC5148101 fatcat:37uu53cnpzfo7h44luqg7zr7ly

Computer-aided detection systems to improve lung cancer early diagnosis: state-of-the-art and challenges

A Traverso, E Lopez Torres, M E Fantacci, P Cerello
2017 Journal of Physics, Conference Series  
In fact, the detection of pulmonary nodule, potential lung cancers, in Computed Tomography scans is a very challenging and time-consuming task for radiologists.  ...  To support radiologists, researchers have developed Computer-Aided Diagnosis (CAD) systems for the automated detection of pulmonary nodules in chest Computed Tomography scans.  ...  of the IRCCS in Candiolo for their contribution to the clinical validation and the Diagnostic Image Analysis Group of the Radboud UMC in Nijmegen for their contribution to the organization of LUNA16.  ... 
doi:10.1088/1742-6596/841/1/012013 fatcat:22ywemcc5fbxvp5nkpcrqv5jpa

Deep Learning for Lung Cancer Nodules Detection and Classification in CT Scans

Diego Riquelme, Moulay A. Akhloufi
2020 AI  
Detecting malignant lung nodules from computed tomography (CT) scans is a hard and time-consuming task for radiologists.  ...  In this work, we review recent state-of-the-art deep learning algorithms and architectures proposed as CAD systems for lung cancer detection.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/ai1010003 fatcat:k7mj3cdzovhhpkxknm34zsw7c4

Potential of Computer-Aided Diagnosis to Improve CT Lung Cancer Screening

N. Lee, A.F. Laine, G. Marquez, J.M. Levsky, J.K. Gohagan
2009 IEEE Reviews in Biomedical Engineering  
There is evidence in published reports that computer-aided diagnosis technology may help radiologists alter the benefit-cost calculus of CT sensitivity and specificity in lung cancer screening protocols  ...  This review will provide insight into the current discussion of the effectiveness of lung cancer screening and assesses the potential of state-of-the-art computer-aided design developments.  ...  ACKNOWLEDGMENT The authors would like to thank A. Stein and L. Lamerato for providing important contextual information of CAD technology and valuable discussions.  ... 
doi:10.1109/rbme.2009.2034022 pmid:22275043 fatcat:qz4rjfyudvguhlvxx5ywu22xku

Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: The LUNA16 challenge

Arnaud Arindra Adiyoso Setio, Alberto Traverso, Thomas de Bel, Moira S.N. Berens, Cas van den Bogaard, Piergiorgio Cerello, Hao Chen, Qi Dou, Maria Evelina Fantacci, Bram Geurts, Robbert van der Gugten, Pheng Ann Heng (+20 others)
2017 Medical Image Analysis  
Automatic detection of pulmonary nodules in thoracic computed tomography (CT) scans has been an active area of research for the last two decades.  ...  In LUNA16, participants develop their algorithm and upload their predictions on 888 CT scans in one of the two tracks: 1) the complete nodule detection track where a complete CAD system should be developed  ...  Acknowledgements The authors acknowledge the National Cancer Institute and the Foundation for the National Institutes of Health and their critical role in the creation of the free publicly available LIDC-IDRI  ... 
doi:10.1016/ pmid:28732268 fatcat:ur26k5wcizbd3khie3bcqptw6q

A Web- and Cloud- based Service for the Clinical Use of a CAD (Computer Aided Detection) System - Automated Detection of Lung Nodules in Thoracic CTs (Computed Tomographies)

M. E. Fantacci, A. Traverso, S. Bagnasco, C. Bracco, D. Campanella, G. Chiara, E. Lopez Torres, A. Manca, D. Regge, M. Saletta, M. Stasi, S. Vallero (+2 others)
2017 Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies  
M5L, a Web-based Computer-Aided Detection (CAD) system to automatically detect lung nodules in thoracic Computed Tomographies, is based on a multi-thread analysis by independent subsystems and the combination  ...  The validation on 1043 scans of 3 independent data-sets showed consistency across data-sets, with a sensitivity of about 80% in the 4-8 range of False Positives per scan, despite varying acquisition and  ...  ACKNOWLEDGEMENTS The authors thank the technical staff of the INFN Computer Centre in Torino, for their contribution in keeping the infrastructure functional at all times.  ... 
doi:10.5220/0006245402020209 dblp:conf/biostec/FantacciTBBCCTM17 fatcat:4f5mbz2wdfb63iheqaimphplna

Detection of Pulmonary Nodules in ct Images Using Deep Learning Technique

Santhi Balachandran, Divya, Nithya Rajendran, Brindha Giri
2020 Journal of Computer Science  
It can be cured if it is diagnosed earlier which decreases the death rate. A computational diagnostic tool named Computer Aided Diagnosis (CAD) is used to detect pulmonary nodules.  ...  It states that the proposed method achieves better accuracy in nodule detection.  ...  Acknowledgement We thank SASTRA Deemed University for providing the research facilities and infrastructure. Author's Contributions All authors are equally contributed in this work and this paper.  ... 
doi:10.3844/jcssp.2020.568.575 fatcat:g4yumia2tnharh6ph3lgia3gyy

PiaNet: A pyramid input augmented convolutional neural network for GGO detection in 3D lung CT scans [article]

Weihua Liu, Xiabi Liua, Xiongbiao Luo, Murong Wang, Guanghui Han, Xinming Zhao, Zheng Zhu
2020 arXiv   pre-print
This paper proposes a new convolutional neural network with multiscale processing for detecting ground-glass opacity (GGO) nodules in 3D computed tomography (CT) images, which is referred to as PiaNet  ...  In the second stage, the pretrained feature-extraction module is loaded into PiaNet, and then PiaNet is fine-tuned using the annotated CT scans.  ...  A promising solution to this problem is the use of computer-aided detection techniques.  ... 
arXiv:2009.05267v2 fatcat:xdxmup6omra47hqdyeqn4fkhqi

Comparing algorithms for automated vessel segmentation in computed tomography scans of the lung: the VESSEL12 study

Rina D. Rudyanto, Sjoerd Kerkstra, Eva M. van Rikxoort, Catalin Fetita, Pierre-Yves Brillet, Christophe Lefevre, Wenzhe Xue, Xiangjun Zhu, Jianming Liang, İlkay Öksüz, Devrim Ünay, Kamuran Kadipaşaogˇlu (+36 others)
2014 Medical Image Analysis  
analysis of the strengths and weaknesses of the various vessel segmentation methods in the presence of various lung diseases.  ...  Our three contributions are: (1) an annotated reference dataset available online for evaluation of new algorithms; (2) a quantitative scoring system for objective comparison of algorithms; and (3) performance  ...  a b s t r a c t The VESSEL12 (VESsel SEgmentation in the Lung) challenge objectively compares the performance of different algorithms to identify vessels in thoracic computed tomography (CT) scans.  ... 
doi:10.1016/ pmid:25113321 pmcid:PMC5153359 fatcat:w5frt2g2rjgepjthnnciba444a
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