NODULe: Combining constrained multi-scale LoG filters with densely dilated 3D deep convolutional neural network for pulmonary nodule detection

Zhang, Junjie; Xia, Yong; Zeng, Haoyue; Zhang, Yanning

doi:10.1016/j.neucom.2018.08.022

Cited by 50 publications

(40 citation statements)

References 38 publications

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“…We showed that [39] 85.6 298,256 335.9 Jacobs et al [40] 36.1 258,075 290.6 Setio et al [41] 31.8 42,281 47.6 Torres et al [42] 76. 8 19,687 22.2 Tan et al [43] 92.9 295,686 333.0 Zhang et al [18] 100.0 45,939 51.7 Setio et al [24] 98.3 754,975 850.2 Wang et al [16] 96.8 53,484 60.2 Our method 95. 4 18,116 20.4 combining the clinical screening method and CNNs is beneficial to improve the performance of nodule detection.…”

Section: Discussionmentioning

confidence: 90%

“…Another approach is that Narayanan et al [17] utilized CT images with different slice thicknesses as training data to detect candidates. To include all the potential nodule candidates, Zhang et al [18] proposed a method, using multi-scale LoG filters to localize nodules. Further, a densely dilated 3-D deep convolutional neural network was applied to reduce the number of false positives.…”

Section: Introductionmentioning

confidence: 99%

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Automatic Pulmonary Nodule Detection in CT Scans Using Convolutional Neural Networks Based on Maximum Intensity Projection

Zheng

Guo

Cui

et al. 2020

IEEE Trans. Med. Imaging

127

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Accurate pulmonary nodule detection is a crucial step in lung cancer screening. Computer-aided detection (CAD) systems are not routinely used by radiologists for pulmonary nodule detection in clinical practice despite their potential benefits. Maximum intensity projection (MIP) images improve the detection of pulmonary nodules in radiological evaluation with computed tomography (CT) scans. Inspired by the clinical methodology of radiologists, we aim to explore the feasibility of applying MIP images to improve the effectiveness of automatic lung nodule detection using convolutional neural networks (CNNs). We propose a CNN-based approach that takes MIP images of different slab thicknesses (5 mm, 10 mm, 15 mm) and 1 mm axial section slices as input. Such an approach augments the two-dimensional (2-D) CT slice images with more representative spatial information that helps discriminate nodules from vessels through their morphologies. Our proposed method achieves sensitivity of 92.7% with 1 false positive per scan and sensitivity of 94.2% with 2 false positives per scan for lung nodule detection on 888 scans in the LIDC-IDRI dataset. The use of thick MIP images helps the detection of small pulmonary nodules (3 mm-10 mm) and results in fewer false positives. Experimental results show that utilizing MIP images can increase the sensitivity and lower the number of false positives, which demonstrates the effectiveness and significance of the proposed MIP-based CNNs framework for automatic pulmonary nodule detection in CT scans. The proposed method also shows the potential that CNNs could gain benefits for nodule detection by combining the clinical procedure.

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Section: Discussionmentioning

confidence: 90%

Section: Introductionmentioning

confidence: 99%

Automatic Pulmonary Nodule Detection in CT Scans Using Convolutional Neural Networks Based on Maximum Intensity Projection

Zheng

Guo

Cui

et al. 2020

IEEE Trans. Med. Imaging

127

View full text Add to dashboard Cite

show abstract

“…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

Self Cite

376

170

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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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“…Gruetzemacher et al [78], used a 3D CNN approach, while the other two authors used a 2D CNN based architecture. From Table 10 it can be seen that Zhang et al [74] and the team of LUNA16FONOVACAD [82] presents the highest CPM with 0.947, obtained by testing on the LUNA16 dataset. Followed by a CPM of 0.876 achieved by Huang et al [89] in the LUNA16/Ali Tianchi dataset.…”

Section: Discussion and Comparative Resultsmentioning

confidence: 99%

“…The NODULe model proposed in Reference [74], uses a conventional method for nodule detection and a deep learning model for nodule identification. A multi-scale Laplacian of Gaussian (LoG) filters and prior shape and size constraints are used to detect nodules.…”

Section: D Deep Learning Approachesmentioning

confidence: 99%

Deep Learning for Lung Cancer Nodules Detection and Classification in CT Scans

Riquelme

Akhloufi

2020

119

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Detecting malignant lung nodules from computed tomography (CT) scans is a hard and time-consuming task for radiologists. To alleviate this burden, computer-aided diagnosis (CAD) systems have been proposed. In recent years, deep learning approaches have shown impressive results outperforming classical methods in various fields. Nowadays, researchers are trying different deep learning techniques to increase the performance of CAD systems in lung cancer screening with computed tomography. In this work, we review recent state-of-the-art deep learning algorithms and architectures proposed as CAD systems for lung cancer detection. They are divided into two categories—(1) Nodule detection systems, which from the original CT scan detect candidate nodules; and (2) False positive reduction systems, which from a set of given candidate nodules classify them into benign or malignant tumors. The main characteristics of the different techniques are presented, and their performance is analyzed. The CT lung datasets available for research are also introduced. Comparison between the different techniques is presented and discussed.

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NODULe: Combining constrained multi-scale LoG filters with densely dilated 3D deep convolutional neural network for pulmonary nodule detection

Cited by 50 publications

References 38 publications

Automatic Pulmonary Nodule Detection in CT Scans Using Convolutional Neural Networks Based on Maximum Intensity Projection

Automatic Pulmonary Nodule Detection in CT Scans Using Convolutional Neural Networks Based on Maximum Intensity Projection

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

Deep Learning for Lung Cancer Nodules Detection and Classification in CT Scans

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