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Frontiers of Medicine

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Front. Med.    2020, Vol. 14 Issue (4) : 450-469    https://doi.org/10.1007/s11684-019-0726-4
REVIEW
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
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Abstract

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.
Keywords deep learning      neural networks      pulmonary medical image      survey     
Corresponding Author(s): Jianlin Wu   
Just Accepted Date: 15 November 2019   Online First Date: 17 December 2019    Issue Date: 26 August 2020
 Cite this article:   
Jiechao Ma,Yang Song,Xi Tian, et al. Survey on deep learning for pulmonary medical imaging[J]. Front. Med., 2020, 14(4): 450-469.
 URL:  
https://academic.hep.com.cn/fmd/EN/10.1007/s11684-019-0726-4
https://academic.hep.com.cn/fmd/EN/Y2020/V14/I4/450
Fig.1  Variety of lesion appearances poses a big challenge for medical image processing. (A) Solid nodule (green box), (B) ground glass nodule (green box), (C) pleural nodule (green box), and (D) the effect of image acquisition.
Authors Year Model name Main method
Hubel et al. [31] 1962 − Proposed the concept of receptive field and found that the animal’s visual nervous system recognizes objects in layers
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
Hecht-Nielsen et al. [41] 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 DBN Improved the difficulty of training the network
Krizhevsky et al. [46] 2012 AlexNet Dropped the top five error rate of the highest accuracy from 26.1% to only 15.3%
Simonyan et al. [47] 2014 VGG-Net VGG can be seen as a deepened version of AlexNet (19 layers)
Szegedy et al. [16] 2014 GoogLeNet Deepening the network (22 layers) and introducing Inception structure instead of simple convolution
He et al. [17] 2015 ResNet Residual module, a design of residual network, allows the network to be trained more deeply (152 layers)
Huang et al. [48] 2016 DenseNet Dense connection: alleviating the problem of gradient disappearance and enhancing feature propagation
Tab.1  Historical perspective on networks
Fig.2  Structure of segmentation network. The left part uses convolution for the extraction of features, and the right part uses deconvolution for the recovery of the original size. The middle part contains skipping connections that are used in U-net.
Fig.3  Structure of classification network. The output are the possibilities of the targets.
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  Deep learning in pulmonary nodule
Fig.4  Illustration of the pipeline for lesion detection. In both training and testing phases, the medical images in DICOM formats are converted and preprocessed to obtain the input images. In the training phase, region proposal methods are used for the extraction of the ROIs of the input images and then adopting classification on the ROIs and output prediction scores. In the testing phase, input images are fed into the trained model generated from the training phase to obtain inference results.
Fig.5  Structure of the detection network. The region proposal network is used to determine the area candidates of objects, and the subsequent structure serves the function of classification.
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  Deep learning in PE
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  Deep learning in pneumonia
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  Deep learning in tuberculosis
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  Deep learning in ILD
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 − 90.1% − 4.0
Tab.7  Deep learning in LIDC-IDRI dataset
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