Automatic detection of multisize pulmonary nodules in CT images: Large‐scale validation of the false‐positive reduction step

Gupta, Anindya; Saar, T.; Märtens, Olev; Moullec, Yannick Le

doi:10.1002/mp.12746

Cited by 41 publications

(22 citation statements)

References 43 publications

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“…Nodules annotated by one or two radiologists, nodules of <3 mm diameter, and non-nodules of 3 mm diameter were defined as "irrelevant findings" and were not included as false-positives or true positives in the analysis. This approach was also used in prior studies (11,21).…”

Section: Internal Validation Using the Lidc-idri Datasetmentioning

confidence: 99%

“…The mark annotated by the model is considered as a true positive if it is located within a distance r from the center of any nodule included in the reference standard, where r is set to the radius of the reference nodule (17). To compute the 95% confidence interval (CI), we performed bootstrapping 1000 times (11).…”

Section: Internal Validation Using the Lidc-idri Datasetmentioning

confidence: 99%

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Development and Validation of a Modified Three-Dimensional U-Net Deep-Learning Model for Automated Detection of Lung Nodules on Chest CT Images From the Lung Image Database Consortium and Japanese Datasets

et al. 2022

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Rationale and Objectives: A more accurate lung nodule detection algorithm is needed. We developed a modified three-dimensional (3D) U-net deep-learning model for the automated detection of lung nodules on chest CT images. The purpose of this study was to evaluate the accuracy of the developed modified 3D U-net deep-learning model. Materials and Methods: In this Health Insurance Portability and Accountability Act-compliant, Institutional Review Board-approved retrospective study, the 3D U-net based deep-learning model was trained using the Lung Image Database Consortium and Image Database Resource Initiative dataset. For internal model validation, we used 89 chest CT scans that were not used for model training. For external model validation, we used 450 chest CT scans taken at an urban university hospital in Japan. Each case included at least one nodule of >5 mm identified by an experienced radiologist. We evaluated model accuracy using the competition performance metric (CPM) (average sensitivity at 1/8, 1/4, 1/2, 1, 2, 4, and 8 false-positives per scan). The 95% confidence interval (CI) was computed by bootstrapping 1000 times. Results: In the internal validation, the CPM was 94.7% (95% CI: 89.1%À98.6%). In the external validation, the CPM was 83.3% (95% CI: 79.4%À86.1%). Conclusion: The modified 3D U-net deep-learning model showed high performance in both internal and external validation.

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Section: Internal Validation Using the Lidc-idri Datasetmentioning

confidence: 99%

Section: Internal Validation Using the Lidc-idri Datasetmentioning

confidence: 99%

Development and Validation of a Modified Three-Dimensional U-Net Deep-Learning Model for Automated Detection of Lung Nodules on Chest CT Images From the Lung Image Database Consortium and Japanese Datasets

et al. 2022

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“…The hand-craft features, e.g., spherical filter and local binary feature [26], are adopted by earlier lung nodule detectors [27]- [31], which achieve inferior performance compared with deep learning-based methods [5], [8], [11], [32]. Ding et al [11] employ a 2D Faster R-CNN as a nodule detector and use 3D CNN for false positive reduction, which achieves 89.1% average sensitivity.…”

Section: B Nodule Detection In 3d Medical Imagesmentioning

confidence: 99%

“…Khosravan and Bagci [8] design a 3D CNN with dense connection and propose adopting max-pooling throughout the network to achieve better performance. Another line of research [7], [10], [13], [32], [33] investigate multi-scale feature maps either in image or feature pyramid to cope with the variance of nodule size. All mentioned nodule detectors use anchors sampled uniformly over the spatial position as candidate nodules and classify each anchor to be a nodule or not as well as adjust their locations.…”

Section: B Nodule Detection In 3d Medical Imagesmentioning

confidence: 99%

Towards Accurate Pulmonary Nodule Detection by Representing Nodules as Points With High-Resolution Network

et al. 2020

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Almost all successful nodule detectors rely heavily on a fixed set of anchor boxes. In this paper, inspired by the success of the keypoint estimation method in natural image detection, we propose an anchor-free framework for accurate pulmonary nodule detection. We first present a novel representation for detecting nodules, in terms of their 3D center locations, which reduces the number of hyper-parameters and the corresponding computation related to anchors, thus making the nodule detection pipeline much simpler. Then, an effective two-stream network is introduced to reduce the false positive nodule candidates, by aggregating information from the image stream and motion-history stream. Experiments show that the proposed approach achieves a sensitivity of 96.1%, with 8 false positives per scan, and a CPM score of 90.6%, on the publicly available LUNA16 dataset, which outperforms other state-of-the-art methods. By testing on the SPIE-AAPM dataset with models pre-trained on the LUNA16, our proposed method yields 92.8% sensitivity with 8 false positives per scan. This demonstrates the effectiveness and generalization ability of our method.

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“…So far, most researches were based on one consolidated dataset. Gupta et al (Gupta et al, 2018) presented a two-stage approach for detection of multi-size pulmonary nodules in CT images from multiple heterogeneous datasets. The aim of the first stage was to initially extract different-sized nodule candidates by lung segmentation, morphological closing process, and rule-based thresholding algorithms.…”

Section: Backpropagation Neural Networkmentioning

confidence: 99%

Evolving the pulmonary nodules diagnosis from classical approaches to deep learning-aided decision support: three decades’ development course and future prospect

Liu

Chi

et al. 2019

J Cancer Res Clin Oncol

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Highlights► It covers the literature of the past thirty years' development in computer-assisted diagnosis of lung nodules.► A summary of algorithms from classical approaches to deep learning on pulmonary nodules diagnosis is provided.► Challenges and opportunities in learning models, algorithms and schemes are highlighted. AbstractLung cancer is the commonest cause of cancer deaths worldwide, and its mortality can be reduced significantly by performing early diagnosis and screening. Since the 1960s, driven by the pressing needs to accurately and effectively interpret the massive volume of chest images generated daily, computer-assisted diagnosis of pulmonary nodule has opened up new opportunities to relax the limitation from physicians' subjectivity, experiences and fatigue. And the fair access to the reliable and affordable computer-assisted diagnosis will fight the inequalities in incidence and mortality between populations. It has been witnessed that significant and remarkable advances have been achieved since the 1980s, and consistent endeavors have been exerted to deal with the grand challenges on how to accurately detect the pulmonary nodules with high sensitivity at low false-positives rate as well as on how to precisely differentiate between benign and malignant nodules. The main goal of this investigation is to provide a comprehensive state-of-the-art review of the computer-assisted nodules detection and benign-malignant classification techniques developed over three decades, which have evolved from the complicated ad hoc analysis pipeline of conventional approaches to the simplified seamlessly integrated deep learning techniques. This review also identifies challenges and highlights opportunities for future work in learning models, learning algorithms and enhancement schemes for bridging current state to future prospect and satisfying future demand. As far as the authors know, it is the first review of the literature of the past thirty years' development in computer-assisted diagnosis of lung nodules. We acknowledge the value of potential multidisciplinary researches that will make the computer-assisted diagnosis of pulmonary nodules enter into the main stream of clinical medicine, and raise the state-of-the-art clinical applications as well as increase both welfares of physicians and patients.

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Automatic detection of multisize pulmonary nodules in CT images: Large‐scale validation of the false‐positive reduction step

Abstract: The authors conclude that the proposed CAD system can identify dissimilar nodule candidates in the multiple heterogeneous datasets. It could be considered as a useful tool to support radiologists during screening trials.

Cited by 41 publications

References 43 publications

Development and Validation of a Modified Three-Dimensional U-Net Deep-Learning Model for Automated Detection of Lung Nodules on Chest CT Images From the Lung Image Database Consortium and Japanese Datasets

Development and Validation of a Modified Three-Dimensional U-Net Deep-Learning Model for Automated Detection of Lung Nodules on Chest CT Images From the Lung Image Database Consortium and Japanese Datasets

Towards Accurate Pulmonary Nodule Detection by Representing Nodules as Points With High-Resolution Network

Evolving the pulmonary nodules diagnosis from classical approaches to deep learning-aided decision support: three decades’ development course and future prospect

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