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Comparing and combining algorithms for computer-aided detection of pulmonary nodules in computed tomography scans: The ANODE09 study
2010
Medical Image Analysis
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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:// anode09.isi.uu.nl), 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/j.media.2010.05.005
pmid:20573538
fatcat:mj2cw6si4ze25kwb4qycnkykie
Analysis of various classification techniques for computer aided detection system of pulmonary nodules in CT
2016
2016 IEEE National Aerospace and Electronics Conference (NAECON) and Ohio Innovation Summit (OIS)
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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]
2020
arXiv
pre-print
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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
2020
IEEE Access
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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
2014
BioMedical Engineering OnLine
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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
2019
Diagnostics
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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
2015
Medical Physics (Lancaster)
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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
2017
Journal of Physics, Conference Series
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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
2020
AI
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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
2009
IEEE Reviews in Biomedical Engineering
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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
2017
Medical Image Analysis
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2017 pre-print arXiv:1612.08012v3 fatcat:6r3yw6issvhrvfl6fqwfuhwh6eOther Versions
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/j.media.2017.06.015
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)
2017
Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies
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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
2020
Journal of Computer Science
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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]
2020
arXiv
pre-print
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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
2014
Medical Image Analysis
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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/j.media.2014.07.003
pmid:25113321
pmcid:PMC5153359
fatcat:w5frt2g2rjgepjthnnciba444a
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