Survey on deep learning for pulmonary medical imaging
Jiechao Ma1, Yang Song2, Xi Tian1, Yiting Hua1, Rongguo Zhang1, Jianlin Wu3()
1. InferVision, Beijing 100020, China 2. Dalian Municipal Central Hospital Affiliated to Dalian Medical University, Dalian 116033, China 3. Affiliated Zhongshan Hospital of Dalian University, Dalian 116001, China
As a promising method in artificial intelligence, deep learning has been proven successful in several domains ranging from acoustics and images to natural language processing. With medical imaging becoming an important part of disease screening and diagnosis, deep learning-based approaches have emerged as powerful techniques in medical image areas. In this process, feature representations are learned directly and automatically from data, leading to remarkable breakthroughs in the medical field. Deep learning has been widely applied in medical imaging for improved image analysis. This paper reviews the major deep learning techniques in this time of rapid evolution and summarizes some of its key contributions and state-of-the-art outcomes. The topics include classification, detection, and segmentation tasks on medical image analysis with respect to pulmonary medical images, datasets, and benchmarks. A comprehensive overview of these methods implemented on various lung diseases consisting of pulmonary nodule diseases, pulmonary embolism, pneumonia, and interstitial lung disease is also provided. Lastly, the application of deep learning techniques to the medical image and an analysis of their future challenges and potential directions are discussed.
. [J]. Frontiers of Medicine, 2020, 14(4): 450-469.
Jiechao Ma, Yang Song, Xi Tian, Yiting Hua, Rongguo Zhang, Jianlin Wu. Survey on deep learning for pulmonary medical imaging. Front. Med., 2020, 14(4): 450-469.
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Blakemore [33]
1971
−
Receptive field of visual cells is acquired rather than innate
Fukushima et al. [34]
1980
Neocognitron
First implementation network of CNN and first application of the concept of receptive field
Fukushima et al. [35]
1984
Neocognitron
Neocognitron machine with double C-layers
Rumelhart et al. [42]
1988
−
Proposed the BP algorithm
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1989
−
Demonstrated that a continuous function in any closed interval can be approximated using a three-layer network of hidden layers
LeCun [43]
1989
CNN
Use of weight sharing and SGD in network optimization
LeCun et al. [44]
1998
LeNet
Defined the basic architecture of CNN
Hinton et al. [45]
2006
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Improved the difficulty of training the network
Krizhevsky et al. [46]
2012
AlexNet
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Simonyan et al. [47]
2014
VGG-Net
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Szegedy et al. [16]
2014
GoogLeNet
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He et al. [17]
2015
ResNet
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2016
DenseNet
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Tab.1
Fig.2
Fig.3
Authors
Year
Task
Modality
2D/3D
Main methods
Li et al. [70]
2016
Nodule type classification
CT
2D
Recognition of three types of nodules
Netto et al. [71]
2012
Nodule type classification
CT
3D
The 3D pulmonary nodules were extracted from the lungs (including mediastinum and chest wall), and then classified
Pei et al. [72]
2010
Nodule type classification
CT
2D
Geometrically constrained region growth method was used for dividing nodule types
Suzuki et al. [73,74]
2005
Benign/malignant classification
LDCT
2D
Distinguished malignant nodules from six different types of benign nodules
Causey et al. [75]
2018
Benign/malignant classification
CT
2D, 3D
Compared deep learning and radiomics approaches for lung nodule malignancy prediction
Xie et al. [77]
2017
Benign/malignant classification
CT
3D
Proposed a transferable multi-model ensemble algorithm for benign-malignant lung nodule classification on chest CT
Shen et al. [78]
2017
Benign/malignant classification
CT
2D, 3D
Cropped different regions from convolutional feature maps and then applied max-pooling different times
Liu et al. [79,80]
2018
Benign/malignant classification
CT
2D
Explored the relationship between lung nodule classification and attribute score
Liao et al. [81]
2019
Benign/malignant classification
CT
3D
Detected all suspicious lesions (pulmonary nodules) and evaluated the whole-lung/pulmonary malignancy
Ding et al. [85]
2017
Nodule detection
CT
3D
Proposed a deconvolutional structure for faster region-based CNN
Winkels et al. [86]
2017
Nodule detection
CT
3D
Used 3D roto-translation group convolutions (G-Convs) instead of the more conventional translational convolutions
Zhu et al. [87]
2017
Nodule detection
CT
3D
Designed a 3D gaster R-CNN nodule detection with a U-net-like encoder-decoder structure for effectively learning nodule features
Tang et al. [88]
2018
Nodule detection
CT
3D
Introduced a novel DCNN approach, consisting of two stages that are fully 3D and end-to-end
Tang et al. [89]
2019
Nodule detection
LDCT
3D
Integrated nodule candidate screening and false positive reduction into one model, trained jointly
Xie et al. [90]
2018
Nodule detection
CT
3D
Modification of the ResNet and feature pyramid network combined, powered by RReLU activation
Ma et al. [91]
2019
Nodule detection
CT
2D, 3D
Used group convolution and attention network to abstract feature and balance the samples with hard negative sample mining
Feng et al. [92]
2017
Nodule segmentation
CT
3D
Used weakly supervised method that generates accurate voxel-level nodule segmentation
Messay et al. [93]
2015
Nodule segmentation
CT
3D
Used weakly labeled data without dense voxel-level annotations
Tab.2
Fig.4
Fig.5
Authors
Year
Task
Modality
Main methods
Rucco et al. [95]
2015
Classification
X-ray
Introduced an approach for the analysis of partial and incomplete datasets based on Q-analysis
Bi et al. [96]
2007
Detection
X-ray
Detected PE from CTPA images
Agharezaei et al. [97]
2016
Classification
X-ray
Predicted the risk level of PE
Serpen et al. [98]
2008
Classification
X-ray
Used knowledge-based hybrid learning algorithm
Tsai et al. [99]
2010
Classification
X-ray
Used GNN network to achieve the PE recognition
Tajbakhsh et al. [100]
2015
Classification
X-ray
Investigated the possibility of a unique PE representation
Chen et al. [101]
2017
Classification
X-ray
Classified free-text radiology reports
Tab.3
Authors
Year
Task
Modality
Main methods
Lee et al. [119]
2001
Detection
CT
Used a template matching algorithm for the identification of the type of pneumonia
Abdullah et al. [120]
2011
Detection
CT
Proposed a detection method of pneumonia symptoms gray-scale color and the segmentation between normal and lung regions
Correa et al. [121]
2018
Classification
Ultrasound
Automatic classification of pneumonia approach based on the analysis of brightness distribution patterns present in rectangular segments
Cisnerosvelarde et al. [122]
2016
Detection
Ultrasound
Proposed the application of ultrasound video analysis for the detection of pneumonia
Sharma et al. [123]
2017
Detection
X-ray
Used Otsu threshold to segregate the healthy part of lung from the pneumonia infected cloudy regions
de Melo et al. [124]
2018
Detection
X-ray
Used parallel technique to improve the computing speed
Wang et al. [125]
2017
Classification
X-ray
Built a large-scale and high-accuracy CAD system
Tab.4
Authors
Year
Task
Modality
Main methods
Pande et al. [135]
2015
Classification
X-ray
Evaluated the accuracy of CAD software for diagnosis of PTB
Rohilla et al. [136]
2017
Classification
X-ray
Used various CNN models to classify the CXR
Lakhani et al. [137]
2017
Classification
X-ray
Used deep learning with CNNs and got the accurately classify tuberculosis
Melendez et al. [138]
2016
Classification
X-ray
Evaluated this framework on a database containing 392 patient records from suspected TB subjects
Melendez et al. [139]
2014
Detection
X-ray
Proposed a method which uses a weakly labeled approach to detect TB
Shin et al. [140]
2016
Detection
X-ray
Presented a deep learning model to detect a disease from an image and annotate its contexts
Murphy et al. [141]
2019
Detection
X-ray
Automated analysis of chest X-ray (CXR) as a sensitive and inexpensive means of screening susceptible populations for pulmonary tuberculosis
Zheng et al. [142]
2017
Detection
X-ray
Found that shallow features or early layers always provide higher detection accuracy
Bar et al. [143]
2015
Detection
X-ray
Explored the ability of CNN learned from a nonmedical dataset to identify different types of pathologies in chest X-rays
Tab.5
Authors
Year
Task
Modality
Main methods
Anthimopoulos et al. [55]
2016
Classification
CT
Proposed and evaluated a CNN for the classification of ILD patterns
Simonyan and Zisserman [147]
2018
Classification
HRCT
Proposed and developed a framework in which CNN was used for tissue categorization of ILD
Li et al. [148]
2013
Classification
HRCT
Used unsupervised algorithm for capturing image features of different scales
Li et al. [149]
2014
Classification
HRCT
Proposed a customized CNN architecture to classify HRCT lung image patches of ILD patterns
Gao et al. [150]
2016
Classification
CT
Proposed multi-label, multi-class ILD model and trained simultaneously
Christodoulidis et al. [151]
2016
Classification
CT
Used multiple transfer of knowledge to improve the accuracy and stability of a CNN on the task of lung tissue pattern classification
Gao et al. [152]
2018
Classification
CT
Proved that the use of three attenuation ranges data can enhance the classification effect
Tab.6
Authors
Year
Datasets
Main methods
Acc.
Sen.
Spc.
FPs/Scan
Armato et al. [161]
2019
LIDC-IDRI
Combined geometric texture with directional gradient histogram with feature reduction of principal component analysis (HOG-PCA) to automatically detect nodules
99.2%
98.3%
98.0%
3.3
Huidrom et al. [162]
2019
LIDC-IDRI
Used a nonlinear algorithm to classify the 3D nodule candidate boxes
93.23%
93.26%
93.2%
−
Shaukat et al. [163]
2019
LIDC-IDRI
Presented a marker-controlled watershed technique that uses intensity, shape and texture features to detect lung nodules
93.7%
95.5%
94.28%
5.72
Zhang et al. [164]
2018
LIDC-IDRI
Used 3D skeletonization feature based prior anatomical knowledge
−
89.3%
−
2.1
Naqi et al. [165]
2018
LIDC-IDRI
Used traditional manual feature HOG and CNN feature to construct hybrid feature vectors
98.8%
97.7%
96.2%
3.8
Liu et al. [166]
2017
LIDC-IDRI
Presented a fast segmentation method for true nodules and false positive nodules
93.20%
92.40%
94.80%
4.5
Javaid et al. [167]
2016
LIDC-IDRI
Extracted 2D and 3D feature sets for nodules to eliminate false positives
96.22%
91.65%
−
3.19
Akram et al. [168]
2015
LIDC-IDR
Proposed a novel pulmonary nodule detection technique by thresholding, label masking, background removal and contour correction
97.52%
95.31%
99.73%
−
Dou et al. [175]
2016
LUNA16
Used 3D CNNs for false positive reduction
−
90.7%
−
4.0
Setio et al. [57]
2016
LUNA16
Used multi-view convolutional networks (ConvNets) to extract the features
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