CNN models discriminating between pulmonary micro-nodules and non-nodules from CT images

Monkam, Patrice; Qi, Shouliang; Xu, Mingyu; Han, Fangfang; Zhao, Xin; Qian, Wei

doi:10.1186/s12938-018-0529-x

Cited by 47 publications

(24 citation statements)

References 38 publications

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“…Recently, Keceli have utilized the pretrained deep CNNs to extract features from five 2D views of 3D volume for the action recognition [55]. The common ways to transfer 3D to 2D include the combination of axial, coronal and sagittal images, several axial slices, several canonical CT slices [30], [56], [57]. Given our dataset is relatively small, only includes 280 subjects, we adopted 2D snapshots of 3D airway trees from ventral, dorsal and isometric views.…”

Section: Multi-view Snapshots Vs Single-view Snapshotmentioning

confidence: 99%

Identification of COPD From Multi-View Snapshots of 3D Lung Airway Tree via Deep CNN

Feng³

et al. 2020

IEEE Access

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Chronic obstructive pulmonary disease (COPD) is associated with morphologic abnormalities of airways with various patterns and severities. However, the way of effectively representing these abnormalities is lacking and whether these abnormalities enable to distinguish COPD from healthy controls is unknown. We propose to use deep convolutional neural network (CNN) to assess 3D lung airway tree from the perspective of computer vision, thereby constructing models of identifying COPD. After extracting airway trees from CT images, snapshots of their 3D visualizations are obtained from ventral, dorsal and isometric views. Using snapshots of each view, one deep CNN model is constructed and further optimized by Bayesian optimization algorithm to indentify COPD. The majority voting of three views presents the final prediction. Finally, the class-discriminative localization maps have been drawn to visually explain the CNNs' decisions. The models trained with single view (ventral, dorsal and isometric) of colorful snapshots present the similar accuracy (ACC) (86.8%, 87.5% and 86.7%) and the model after voting achieves the ACC of 88.2%. The ACC of the final voting model using gray and binary snapshots achieves 88.6% and 86.4%, respectively. Our specially designed CNNs outperform the typical off-the-shelf CNNs and the pre-trained CNNs with fine tuning. The class-discriminative regions of COPD are mainly located at central airways; however, regions in HC are scattering and located at peripheral airways. It is feasible to identify COPD using snapshots of 3D lung airway tree extracted from CT images via deep CNN. The CNNs can represent the abnormalities of airway tree in COPD and make accurate CT-based diagnosis of COPD. INDEX TERMS Chronic obstructive pulmonary disease (COPD), deep learning, convolutional neural networks, computed tomography (CT), airway, image classification.

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Section: Multi-view Snapshots Vs Single-view Snapshotmentioning

confidence: 99%

Identification of COPD From Multi-View Snapshots of 3D Lung Airway Tree via Deep CNN

Feng³

et al. 2020

IEEE Access

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“…The CT scans provided by the Lung Image Database Consortium and Image Database Resource Initiative (LIDC/IDRI) were used to assess the efficiency and effectiveness of the system developed in this study [34]. LIDC/IDRI is a publicly accessible medical images database consisting of 1,018 CT scans produced from 1,010 different patients where the scans of eight patients had been inadvertently duplicated [35]. Each patient's file consists of a set of images originated from a thoracic CT scan and a corresponding XML file.…”

Section: Methodsmentioning

confidence: 99%

Ensemble Learning of Multiple-View 3D-CNNs Model for Micro-Nodules Identification in CT Images

Monkam

et al. 2019

IEEE Access

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Numerous automatic systems of pulmonary nodules detection have been proposed, but very few of them have been consecrated to micro-nodules (diameter < 3 mm) even though they are regarded as the earliest manifestations of lung cancer. Moreover, most available systems present high false positive rate resulting from their incapability of discriminating between micro-nodules and non-nodules. Thus, this paper proposes a system to differentiate between the micro-nodules and non-nodules in computed tomography (CT) images by an ensemble learning of multiple-view 3-D convolutional neural networks (3D-CNNs). A total of 34 494 volumetric image samples, including 13 179 micro-nodules and 21 315 non-nodules, are acquired from the 1010 CT scans of the LIDC/IDRI database. The pulmonary nodule candidates are cropped with five different sizes, including 20 × 20 × 3, 16 × 16 × 3, 12 × 12 × 3, 8 × 8 × 3, and 4 × 4 × 3. Then, five distinct 3D-CNN models are built and implemented on one size of the nodule candidates. An extreme learning machine (ELM) network is utilized to integrate the five 3D-CNN outputs, yielding the final classification results. The performance of the proposed system is assessed in terms of accuracy, area under the curve (AUC), F-score, and sensitivity. It is found that the proposed system yields an accuracy, AUC, F-score, and sensitivity of 97.35%, 0.98, 96.42%, and 96.57%, respectively. These performances are highly superior to those of 2D-CNNs, single 3D-CNN model, as well as those by the state-of-the-art methods implemented on the same dataset. For the ensemble method, ELM achieves better performance than the majority voting, averaging, AND operator, and autoencoder. The results demonstrate that developing an automatic system for discriminating between micro-nodules and non-nodules in CT images is feasible, which extends lung cancer studies to micro-nodules. The combination of multiple-view 3D-CNNs and ensemble learning contribute to excellent identification performance, and this strategy may help develop other reliable clinical-decision support systems. INDEX TERMS Computed tomography (CT) images, 3D convolutional neural networks, ensemble learning, image classification, lung cancer, micro-nodules.

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“…Over the last few decades, several attempts have been made to apply DL to human health and medicine [19,20], mostly in the field of diagnostic imaging [21][22][23]. To the best of our knowledge, AI has never been applied to the identification and quantification of gross lesions in animals.…”

Section: Introductionmentioning

confidence: 99%

Scoring pleurisy in slaughtered pigs using convolutional neural networks

Trachtman

Bergamini

Palazzi

et al. 2020

Vet Res

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Diseases of the respiratory system are known to negatively impact the profitability of the pig industry, worldwide. Considering the relatively short lifespan of pigs, lesions can be still evident at slaughter, where they can be usefully recorded and scored. Therefore, the slaughterhouse represents a key check-point to assess the health status of pigs, providing unique and valuable feedback to the farm, as well as an important source of data for epidemiological studies. Although relevant, scoring lesions in slaughtered pigs represents a very time-consuming and costly activity, thus making difficult their systematic recording. The present study has been carried out to train a convolutional neural network-based system to automatically score pleurisy in slaughtered pigs. The automation of such a process would be extremely helpful to enable a systematic examination of all slaughtered livestock. Overall, our data indicate that the proposed system is well able to differentiate half carcasses affected with pleurisy from healthy ones, with an overall accuracy of 85.5%. The system was better able to recognize severely affected half carcasses as compared with those showing less severe lesions. The training of convolutional neural networks to identify and score pneumonia, on the one hand, and the achievement of trials in large capacity slaughterhouses, on the other, represent the natural pursuance of the present study. As a result, convolutional neural network-based technologies could provide a fast and cheap tool to systematically record lesions in slaughtered pigs, thus supplying an enormous amount of useful data to all stakeholders in the pig industry. © The Author(s) 2020. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article' s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article'

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CNN models discriminating between pulmonary micro-nodules and non-nodules from CT images

Cited by 47 publications

References 38 publications

Identification of COPD From Multi-View Snapshots of 3D Lung Airway Tree via Deep CNN

Identification of COPD From Multi-View Snapshots of 3D Lung Airway Tree via Deep CNN

Ensemble Learning of Multiple-View 3D-CNNs Model for Micro-Nodules Identification in CT Images

Scoring pleurisy in slaughtered pigs using convolutional neural networks

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