A CAD system for nodule detection in low‐dose lung CTs based on region growing and a new active contour model

Bellotti, R.; Carlo, Francesco De; Gargano, G.; Tangaro, Sabina; Cascio, Donato; Catanzariti, Ezio; Cerello, Piergiorgio; Cheran, S. C.; Delogu, Pasquale; Mitri, I. De; Fulcheri, C.; Grosso, Daniele; Retico, Alessandra; Squarcia, S.; Tommasi, Eleonora; Golosio, Bruno

doi:10.1118/1.2804720

Cited by 95 publications

(53 citation statements)

References 25 publications

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“…It is difficult to establish comparison among CAD schemes tested over different databases [5] : there should be an independent and standardized nodule database for all these studies; the algorithms for automated detection of the different studies are also different…; however, our results are in good agreement with those of Retico et al [12] , who reported 80% of sensitivity and 10 FPs/scan; or Belloti et al [11] who presented 80% at 2.47 FPs/ using a neural network classifier to reduce the false positive rate. Pu et al [10] have also presented a sensitivity of 81.5% with 6.5 FP identifications per examination.…”

Section: Discussionsupporting

confidence: 72%

“…It has been shown that CAD for lung nodule detection could increase radiologists' performance [5][6][7] . Different approaches have been proposed for automatic detection of lung cancer on CT around the world: while some have evaluated different marketed CAD systems, other investigators proposed other methods and processing techniques [5][6][7][8][9][10][11][12][13][14][15] .…”

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confidence: 99%

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Automatic detection of pulmonary nodules: Evaluation of performance using two different MDCT scanners

Bayarri

Suárez-Cuenca²,

Tahoces³

et al. 2012

JBGC

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The purpose of this study was to evaluate the diagnostic performance of a computer-aided diagnosis (CAD) system, on the detection of pulmonary nodules in multidetector row computed tomography (MDCT) images, by using two different MDCT scanners. The computerized scheme was based on the iris filter. We have collected CT cases of patients with pulmonary nodules. We have included in the study one hundred and thirty-two calcified and noncalcified nodules, measuring 4-30 mm in diameter. CT examinations were performed by using two different equipments: a CT scanner (SOMATOM Emotion 6), and a dual-source computed tomography system (SOMATOM Definition) (Siemens Medical System, Forchheim, Germany), with the following parameters: collimation, 6x1.0mm (Emotion 6); and 64×0.6mm (Definition); 100-130 kV; 70-110 mAs. Data were reconstructed with a slice thickness of 1.25mm (Emotion 6) and 1mm (Definition). True positive cases were determined by an independent interpretation of the study by three experienced chest radiologists, the panel decision being used as the reference standard. Free-response Receiver Operating Characteristic curves, sensitivity and number of false-positive per scan, were calculated. Our CAD scheme, for the test set of the study, yielded a sensitivity of 80%, with an average of 5.2 FPs per examination. At an average false positive rate of 9 per scan, our CAD scheme achieved sensitivities of 94% for all nodules, 94.5% for solid, 80% for non-solid, 84% for spiculated, and 97% for non-spiculated nodules. These encouraging results suggest that our CAD system, advocated as a second reader, may help radiologists in the detection of lung nodules in MDCT.

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Section: Discussionsupporting

confidence: 72%

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confidence: 99%

Automatic detection of pulmonary nodules: Evaluation of performance using two different MDCT scanners

Bayarri

Suárez-Cuenca²,

Tahoces³

et al. 2012

JBGC

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“…Computational classifier Santos et al [4], Wang et al [25], Choi and Choi [24], Riccardi et al [30], Liu et al [31], Ozekes and Osman [37], Yang, Periaswamy and Wu [39] and Orozco et al [94] Support vector machines (SVM) El-Baz et al [22] Bayesian supervised Cascio et al [26], Ashwin et al [72], Lin et al [95] and Bellotti et al [96] Artificial neural networks (ANN)…”

Section: Authorsmentioning

confidence: 99%

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

Valente

Cortez

Neto

et al. 2016

Computer Methods and Programs in Biomedicine

200

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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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“…It is, therefore, necessary to acquire the intraprocedural images at a lower dose, such that the net radiation dose is within the safe limits. There are some previous studies which have reported use of low-dose CT [175][176][177], however, these techniques were primarily employed for diagnostic and computer-aided detection applications and did not involve image registration.…”

Section: Motivationmentioning

confidence: 99%

High-Performance 3D Image Processing Architectures for Image-Guided Interventions

Dandekar¹

2008

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Minimally invasive image-guided interventions (IGIs) are time and cost efficient, minimize unintended damage to healthy tissues, and lead to faster patient recovery. Advanced three-dimensional (3D) image processing is a critical need for navigation during IGIs. However, achieving on-demand performance, as required by IGIs, for these image processing operations using software-only implementations is challenging because of the sheer size of the 3D images, and memory and compute intensive nature of the operations. This dissertation, therefore, is geared toward developing high-performance 3D image processing architectures, which will enable improved intraprocedural visualization and navigation capabilities during IGIs.In this dissertation we present an architecture for real-time implementation of 3D filtering operations that are commonly employed for preprocessing of medical images. This architecture is approximately two orders of magnitude faster than corresponding software implementations and is capable of processing 3D medical images at their acquisition speeds. Report Documentation PageCombining complementary information through registration between pre-and intraprocedural images is a fundamental need in the IGI workflow. Intensity-based deformable registration, which is completely automatic and locally accurate, is a promising approach to achieve this alignment. These algorithms, however, are extremely compute intensive, which has prevented their clinical use. We present an FPGA-based architecture for accelerated implementation of intensity-based deformable image registration. This high-performance architecture achieves over an order of magnitude speedup when compared with a corresponding software implementation and reduces the execution time of deformable registration from hours to minutes while offering comparable image registration accuracy.Furthermore, we present a framework for multiobjective optimization of finite-precision implementations of signal processing algorithms that takes into account multiple conflicting objectives such as implementation accuracy and hardware resource consumption. The evaluation that we have performed in the context of FPGA-based image registration demonstrates that such an analysis can be used to enhance automated hardware design processes, and efficiently identify a system configuration that meets given design constraints. In addition, we also outline two novel clinical applications that can directly benefit from these developments and demonstrate the feasibility of our approach in the context of these applications. These advances will ultimately enable integration of 3D image processing into clinical workflow.

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A CAD system for nodule detection in low‐dose lung CTs based on region growing and a new active contour model

Cited by 95 publications

References 25 publications

Automatic detection of pulmonary nodules: Evaluation of performance using two different MDCT scanners

Automatic detection of pulmonary nodules: Evaluation of performance using two different MDCT scanners

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

High-Performance 3D Image Processing Architectures for Image-Guided Interventions

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