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

Ginneken, Bram van; Armato, Samuel G.; Hoop, Bartjan de; Vorst, Saskia van Amelsvoort-van de; Duindam, Thomas; Niemeijer, Meindert; Murphy, Keelin; Schilham, Arnold M. R.; Retico, Alessandra; Fantacci, Maria Evelina; Camarlinghi, N.; Bagagli, Francesco; Gori, Ilaria; Hara, Takeshi; Fujita, Hiroshi; Gargano, G.; Bellotti, R.; Tangaro, Sabina; Bolanos, Lourdes; Carlo, Francesco De; Cerello, Piergiorgio; Cheran, S. C.; Torres, E. López; Prokop, Mathias

doi:10.1016/j.media.2010.05.005

Cited by 267 publications

(171 citation statements)

References 67 publications

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“…Generally, these databases are used to train students, to serve as a repository for rare cases, and to allow comparisons between the performance of different CADe systems [61]. Among the more important public databases available are: Lung Image Database Consortium (LIDC) [62,63], Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) [64,65], Early Lung Cancer Action Program (ELCAP) [7], Nederlands Leuvens Longkanker Screeningsonderzoek (NELSON) [66] and Automatic Nodule Detection 2009 (ANODE09) [67,68].…”

Section: Acquisition Of Datamentioning

confidence: 99%

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Automatic 3D pulmonary nodule detection in CT images: A survey

Valente

Cortez

Neto

et al. 2016

Computer Methods and Programs in Biomedicine

202

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This work presents a systematic review of techniques for the 3D automatic detection of pulmonary nodules in computerized-tomography (CT) images.Its main goals are to analyze the latest technology being used for the development of computational diagnostic tools to assist in the acquisition, storage and, mainly, processing and analysis of the biomedical data. Also, this work identifies the progress made, so far, evaluates the challenges to be overcome and provides an analysis of future prospects. As far as the authors know, this is the first time that a review is devoted exclusively to automated 3D techniques for the detection of pulmonary nodules from lung CT images, which makes this work of noteworthy value. The research covered the published works in the Web of Science, PubMed, Science Direct and IEEEXplore up to December 2014. Each work found that referred to automated 3D segmentation of the lungs was individually analyzed to identify its objective, methodology and results. Based on the analysis of the selected works, several studies were seen to be useful for the construction of medical diagnostic * Corresponding author. E-mail: tavares@fe.up.pt. 31, 2015 aid tools. However, there are certain aspects that still require attention such as increasing algorithm sensitivity, reducing the number of false positives, improving and optimizing the algorithm detection of different kinds of nodules with different sizes and shapes and, finally, the ability to integrate with the Electronic Medical Record Systems and Picture Archiving and Communication Systems. Based on this analysis, we can say that further research is needed to develop current techniques and that new algorithms are needed to overcome the identified drawbacks. Preprint submitted to Computer Methods and Programs in Biomedicine August

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Section: Acquisition Of Datamentioning

confidence: 99%

“…The ANODE09 is a database of 55 scans from a lung cancer screening program and a web-based framework for objective evaluation of nodule detection algorithms. Any team can upload results to facilitate benchmarking [66,64,68].…”

Section: Acquisition Of Datamentioning

confidence: 99%

Automatic 3D pulmonary nodule detection in CT images: A survey

Valente

Cortez

Neto

et al. 2016

Computer Methods and Programs in Biomedicine

202

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“…Our datasets are representative, but very challenging with large within-class variations for polyp, nodule class and other anatomical structures in colon and lung volumes. The results validate that this new classification framework can significantly improve the accuracy of our baseline computer-aided detection system, using the same set of input image features, and compare favorably with other state-of-the-arts [1][2][3][4][6][7][8].…”

Section: Introductionmentioning

confidence: 59%

“…However, these two cancers are highly preventable or "curable" if detected early. Image interpretation based cancer detection via 3D computer tomography has emerged as a common clinical practice, and many computer-aided detection tools for enhancing radiologists' diagnostic performance and effectiveness are developed in the last decade [1][2][3][4][6][7][8]. The key for radiologists to accept the clinical usage of a CAD system is highest possible detection sensitivity with reasonably low false positive (FP) rate per case.…”

Section: Introductionmentioning

confidence: 99%

“…more than 100 candidates per scan with one to two true positives. Then dozens or hundreds of heterogeneous image features can be computed per VOI, in domains of volumetric shape, intensity, gradient, texture and even context [1,3,4,7,8]. Lastly, the essential goal for classification is achieving the best balance between high sensitivities and low false positive rates, given VOIs and associated features.…”

Section: Introductionmentioning

confidence: 99%

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Sparse Classification for Computer Aided Diagnosis Using Learned Dictionaries

Liu

Lü

et al. 2011

Lecture Notes in Computer Science

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Abstract. Classification is one of the core problems in computer-aided cancer diagnosis (CAD) via medical image interpretation. High detection sensitivity with reasonably low false positive (FP) rate is essential for any CAD system to be accepted as a valuable or even indispensable tool in radiologists' workflow. In this paper, we propose a novel classification framework based on sparse representation. It first builds an overcomplete dictionary of atoms for each class via K-SVD learning, then classification is formulated as sparse coding which can be solved efficiently. This representation naturally generalizes for both binary and multiwise classification problems, and can be used as a standalone classifier or integrated with an existing decision system. Our method is extensively validated in CAD systems for both colorectal polyp and lung nodule detection, using hospital scale, multi-site clinical datasets. The results show that we achieve superior classification performance than existing stateof-the-arts, using support vector machine (SVM) and its variants [1, 2], boosting [3], logistic regression [4], relevance vector machine (RVM) [5,6], or k-nearest neighbor (KNN) [7].

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A improved detection method for lung nodule based on multi‐scale 3Dconvolutional neural network

Tan

Zhu

et al. 2022

Concurrency and Computation

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Low detection sensitivity and high false positives are two main challenges from traditional lung nodule detection model. In order to improve reliability of detection model, we proposed a multi‐scale three‐dimensional convolutional neural network lung nodule detection method which can simultaneously detect lung nodules and reduce false positives. First, a 3D UNet++‐like architecture with an encoding‐decoding structure is regarded as a feature extraction network and combines it with the region proposal network. The multi‐feature fusion method can fully learn features at different levels through the cross‐layer connection between the encoder and the decoder. Second, the layers of the 3D UNet++‐like architecture are connected by residual blocks, which enhance feature reuse and speeds up the convergence speed of the network. Finally, the three input sizes are input into the 3D neural network, and their classification results are merged, thus to find the final nodule determination result. Based on the LUNA16 dataset, the experiment result shows that average sensitivity is 87.3% and be increased 7.8% than the UNet++ network in our model. And if the number of candidate nodules is 48, the sensitivity is as high as 96.2%, it increases 8.1% than the VGG16 network. Obviously, that the improved model can significantly improve detection sensitivity and reduce false positives, which can provide a valuable theoretical reference for clinical medicine.

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

Cited by 267 publications

References 67 publications

Automatic 3D pulmonary nodule detection in CT images: A survey

Automatic 3D pulmonary nodule detection in CT images: A survey

Sparse Classification for Computer Aided Diagnosis Using Learned Dictionaries

A improved detection method for lung nodule based on multi‐scale 3Dconvolutional neural network

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