Pulmonary nodule detection in medical images: A survey

Zhang, Junjie; Xia, Yong; Cui, Hengfei; Zhang, Yanning

doi:10.1016/j.bspc.2018.01.011

Cited by 70 publications

(32 citation statements)

References 53 publications

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“…Recently, deep learning techniques have achieved profound success in computer vision, since they provide a uniform feature extraction-classification framework to free users from troublesome handcrafted feature extraction [25,26,45,[52][53][54][55][56]. This success has prompted many investigators to employ deep convolutional neural networks (CNNs) in medical image analysis.…”

Section: Introductionmentioning

confidence: 99%

Knowledge-based Collaborative Deep Learning for Benign-Malignant Lung Nodule Classification on Chest CT

Xie

Xia

Zhang

et al. 2019

IEEE Trans. Med. Imaging

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395

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The accurate identification of malignant lung nodules on chest CT is critical for the early detection of lung cancer, which also offers patients the best chance of cure. Deep learning methods have recently been successfully introduced to computer vision problems, although substantial challenges remain in the detection of malignant nodules due to the lack of large training datasets. In this paper, we propose a multi-view knowledge-based collaborative (MV-KBC) deep model to separate malignant from benign nodules using limited chest CT data. Our model learns 3D lung nodule characteristics by decomposing a 3D nodule into nine fixed views. For each view, we construct a knowledge-based collaborative (KBC) submodel, where three types of image patches are designed to fine-tune three pre-trained ResNet-50 networks that characterize the nodules' overall appearance, voxel and shape heterogeneity, respectively. We jointly use the nine KBC submodels to classify lung nodules with an adaptive weighting scheme learned during the error back propagation, which enables the MV-KBC model to be trained in an end-to-end manner. The penalty loss function is used for better reduction of the false negative rate with a minimal effect on the overall performance of the MV-KBC model. We tested our method on the benchmark LIDC-IDRI dataset and compared it to five state-of-the-art classification approaches. Our results show that the MV-KBC model achieved an accuracy of 91.60% for lung nodule classification with an AUC of 95.70%. These results are markedly superior to the state-of-the-art approaches.

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Section: Introductionmentioning

confidence: 99%

Knowledge-based Collaborative Deep Learning for Benign-Malignant Lung Nodule Classification on Chest CT

Xie

Xia

Zhang

et al. 2019

IEEE Trans. Med. Imaging

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395

170

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“…In recent years, although many methods of lung nodule detection has been proposed [7][8][9] , it is still difficult to obtain satisfactory detection result due to the heterogeneity of lung nodules on CT images (as shown in Fig. 1).…”

Section: Introductionmentioning

confidence: 99%

A Two-Stage Convolutional Neural Networks for Lung Nodule Detection

Cao

Liu

et al. 2020

IEEE J. Biomed. Health Inform.

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Early detection of lung cancer is an effective way to improve the survival rate of patients. It is a critical step to have accurate detection of lung nodules in computed tomography (CT) images for the diagnosis of lung cancer. However, due to the heterogeneity of the lung nodules and the complexity of the surrounding environment, robust nodule detection has been a challenging task. In this study, we propose a two-stage convolutional neural network (TSCNN) architecture for lung nodule detection. The CNN architecture in the first stage is based on the improved UNet segmentation network to establish an initial detection of lung nodules. Simultaneously, in order to obtain a high recall rate without introducing excessive false positive nodules, we propose a novel sampling strategy, and use the offline hard mining idea for training and prediction according to the proposed cascaded prediction method. The CNN architecture in the second stage is based on the proposed dual pooling structure, which is built into three 3D CNN classification networks for false positive reduction.Since the network training requires a significant amount of training data, we adopt a data augmentation method based on random mask. Furthermore, we have improved the generalization ability of the false positive reduction model by means of ensemble learning. The proposed method has been experimentally verified on the LUNA dataset.Experimental results show that the proposed TSCNN architecture can obtain competitive detection performance. Keywords: lung nodule detection; UNet; 3D CNN; ensemble learning;computer-aided diagnosis methods in the following aspects: 1) using the improved UNet segmentation model for lung nodule detection; 2) for the segmentation model training, we propose a new sampling strategy and an offline hard mining training approach; 3) we propose a cascade prediction method different from the traditional prediction method; 4) build three 3D CNN classification networks based on the dual pooling method; 5) design a data augmentation method based on random mask. MethodsThe lung nodule detection framework proposed in this paper is divided into two stages. The first stage: the detection of candidate nodules, which is based on the UNet architecture to achieve the detection of candidate nodules by segmenting suspicious nodules. The second stage: the reduction of false positive nodules, which is based on the 3DCNN architecture to eliminate false positive nodules through the integration of multiple models. The overall architecture of the proposed lung nodule detection method is shown in Fig. 2.

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“…There has been an ever-growing body of research on automated detection of pulmonary nodules in medical images. Arguably, the literature can be divided into two families of algorithms: feature engineering-based methods and deep learning-based methods [3]. The literature of the former family of modules features a wide variety of lung segmentation and nodule detection techniques.…”

Section: Introductionmentioning

confidence: 99%

A Two-Stage Framework for Automated Malignant Pulmonary Nodule Detection in CT Scans

2020

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This research is concerned with malignant pulmonary nodule detection (PND) in low-dose CT scans. Due to its crucial role in the early diagnosis of lung cancer, PND has considerable potential in improving the survival rate of patients. We propose a two-stage framework that exploits the ever-growing advances in deep neural network models, and that is comprised of a semantic segmentation stage followed by localization and classification. We employ the recently published DeepLab model for semantic segmentation, and we show that it significantly improves the accuracy of nodule detection compared to the classical U-Net model and its most recent variants. Using the widely adopted Lung Nodule Analysis dataset (LUNA16), we evaluate the performance of the semantic segmentation stage by adopting two network backbones, namely, MobileNet-V2 and Xception. We present the impact of various model training parameters and the computational time on the detection accuracy, featuring a 79.1% mean intersection-over-union (mIoU) and an 88.34% dice coefficient. This represents a mIoU increase of 60% and a dice coefficient increase of 30% compared to U-Net. The second stage involves feeding the output of the DeepLab-based semantic segmentation to a localization-then-classification stage. The second stage is realized using Faster RCNN and SSD, with an Inception-V2 as a backbone. On LUNA16, the two-stage framework attained a sensitivity of 96.4%, outperforming other recent models in the literature, including deep models. Finally, we show that adopting a transfer learning approach, particularly, the DeepLab model weights of the first stage of the framework, to infer binary (malignant-benign) labels on the Kaggle dataset for pulmonary nodules achieves a classification accuracy of 95.66%, which represents approximately 4% improvement over the recent literature.

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Pulmonary nodule detection in medical images: A survey

Cited by 70 publications

References 53 publications

Knowledge-based Collaborative Deep Learning for Benign-Malignant Lung Nodule Classification on Chest CT

Knowledge-based Collaborative Deep Learning for Benign-Malignant Lung Nodule Classification on Chest CT

A Two-Stage Convolutional Neural Networks for Lung Nodule Detection

A Two-Stage Framework for Automated Malignant Pulmonary Nodule Detection in CT Scans

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