Lung Nodule and Cancer Detection in Computed Tomography Screening

Rubin, Geoffrey D.

doi:10.1097/rti.0000000000000140

Cited by 105 publications

(70 citation statements)

References 67 publications

(80 reference statements)

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“…Lastly, for identification of indeterminate pulmonary nodules, we relied on identification of the same nodule by two reference readers using clinical dose settings. While this methodology can result in substantial variability in estimates of reader performance (39, 40), we are estimating the effect of dose on performance, so the relative performance of the readers across the dose levels (rather than the absolute estimate) is the variable of interest.…”

Section: Discussionmentioning

confidence: 99%

Estimation of Observer Performance for Reduced Radiation Dose Levels in CT

Fletcher

Fidler

et al. 2017

Academic Radiology

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Section: Discussionmentioning

confidence: 99%

Estimation of Observer Performance for Reduced Radiation Dose Levels in CT

Fletcher

Fidler

et al. 2017

Academic Radiology

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“…). These small and faint nodules are often missed by radiologists or CAD systems . Second, vessels are the dominant normal anatomy within the lungs often having similar CT values and cross‐sectional shapes as those of nodules.…”

Section: Introductionmentioning

confidence: 99%

“…These small and faint nodules are often missed by radiologists or CAD systems. 16,17 Second, vessels are the dominant normal anatomy within the lungs often having similar CT values and cross-sectional shapes as those of nodules. Therefore, vessels and vessel-crossings can be easily misidentified as nodules with increased false positives.…”

Section: Introductionmentioning

confidence: 99%

Segmentation and suppression of pulmonary vessels in low‐dose chest CT scans

Gu¹,

Wang

Zhao³

et al. 2019

Medical Physics

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Purpose The suppression of pulmonary vessels in chest computed tomography (CT) images can enhance the conspicuity of lung nodules, thereby improving the detection rate of early lung cancer. This study aimed to develop two key techniques in vessel suppression, that is, segmentation and removal of pulmonary vessels while preserving the nodules. Methods Pulmonary vessel segmentation and removal methods in CT images were developed. The vessel segmentation method used a framework of two cascaded convolutional neural networks (CNNs). A bi‐class segmentation network was utilized in the first step to extract high‐intensity structures, including both vessels and nonvascular tissues such as nodules. A tri‐class segmentation network was employed in the second step to distinguish the vessels from nonvascular tissues (mainly nodules) and the lung parenchyma. In the vessel removal method, the voxels in the segmented vessels were replaced with randomly selected voxels from the surrounding lung parenchyma. The dataset in this study comprised 50 three‐dimensional (3D) low‐dose chest CT images. The labels for vessel and nodule segmentation were annotated with a semi automatic approach. The two cascaded networks for pulmonary vessel segmentation were trained with CT images of 40 cases and tested with CT images of ten cases. Pulmonary vessels were removed from the ten testing scans based on the predicted segmentation results. In addition to qualitative evaluation to the effects of segmentation and removal, the segmentation results were quantitatively evaluated using Dice coefficient (DICE), Jaccard index (JAC), and volumetric similarity (VS) and the removal results were evaluated using contrast‐to‐noise ratio (CNR). Results In the first step of vessel segmentation, the mean DICE, JAC, and VS for high‐intensity tissues, including both vessels and nodules, were 0.943, 0.893, and 0.991, respectively. In the second step, all the nodules were separated from the vessels, and the mean DICE, JAC, and VS for the vessels were 0.941, 0.890, and 0.991, respectively. After vessel removal, the mean CNR for nodules was improved from 4.23 (6.26 dB) to 6.95 (8.42 dB). Conclusions Quantitative and qualitative evaluations demonstrated that the proposed method achieved a high accuracy for pulmonary vessel segmentation and a good effect on pulmonary vessel suppression.

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“…However, a large number of false positives (i.e., a misclassified non-nodule as a nodule) occur at the cost of high sensitivity. These numerous false positives that require extra attention from radiologists also hinder the practical use of CAD systems [12]. Second, the use of volumetric 3D images requires very high computational cost and huge memory storage, which directly affect the speed of the training and detection processes.…”

Section: Introductionmentioning

confidence: 99%

Single-view 2D CNNs with fully automatic non-nodule categorization for false positive reduction in pulmonary nodule detection

Eun

Kim

Jung

et al. 2018

Computer Methods and Programs in Biomedicine

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Background and Objective: In pulmonary nodule detection, the first stage, candidate detection, aims to detect suspicious pulmonary nodules. However, detected candidates include many false positives and thus in the following stage, false positive reduction, such false positives are reliably reduced. Note that this task is challenging due to 1) the imbalance between the numbers of nodules and non-nodules and 2) the intra-class diversity of non-nodules. Although techniques using 3D convolutional neural networks (CNNs) have shown promising performance, they suffer from high computational complexity which hinders constructing deep networks. To efficiently address these problems, we propose a novel framework using the ensemble of 2D CNNs using single views, which outperforms existing 3D CNN-based methods. Methods: Our ensemble of 2D CNNs utilizes single-view 2D patches to improve both computational and memory efficiency compared to previous techniques exploiting 3D CNNs. We first categorize non-nodules on the basis of features encoded by an autoencoder. Then, all 2D CNNs are trained by using the same nodule samples, but with different types of non-nodules. By extending the learning capability, this training scheme resolves difficulties of extracting representative features from non-nodules with large appearance variations. Note that, instead of manual categorization requiring the heavy workload of radiologists, we propose to automatically categorize non-nodules based on the autoencoder and k-means clustering. Results: We performed extensive experiments to validate the effectiveness of our framework based on the database of the lung nodule analysis 2016 challenge. The superiority of our framework is demonstrated through comparing the performance of five frameworks trained with differently constructed training sets. Our proposed framework achieved state-of-the-art performance (0.922 of the competition performance metric score) with low computational demands (789K of parameters and 1,024M of floating point operations per second). Conclusion: We presented a novel false positive reduction framework, the ensemble of single-view 2D CNNs with fully automatic non-nodule categorization, for pulmonary nodule detection. Unlike previous 3D CNN-based frameworks, we utilized 2D CNNs using 2D single views to improve computational efficiency. Also, our training scheme using categorized non-nodules, extends the learning capability of representative features of different non-nodules. Our framework achieved state-of-the-art performance with low computational complexity.

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Lung Nodule and Cancer Detection in Computed Tomography Screening

Cited by 105 publications

References 67 publications

Estimation of Observer Performance for Reduced Radiation Dose Levels in CT

Estimation of Observer Performance for Reduced Radiation Dose Levels in CT

Segmentation and suppression of pulmonary vessels in low‐dose chest CT scans

Single-view 2D CNNs with fully automatic non-nodule categorization for false positive reduction in pulmonary nodule detection

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