Detection of pulmonary micronodules in computed tomography images and false positive reduction using 3D convolutional neural networks

Manual detection of small uncalcified pulmonary nodules (diameter <4 mm) in thoracic computed tomography (CT) scans is a tedious and error‐prone task. Automatic detection of disperse micronodules is, thus, highly desirable for improved characterization of the fatal and incurable occupational pulmonary diseases. Here, we present a novel computer‐assisted detection (CAD) scheme specifically dedicated to detect micronodules. The proposed scheme consists of a candidate‐screening module and a false positive (FP) reduction module. The candidate‐screening module is initiated by a lung segmentation algorithm and is followed by a combination of 2D/3D features‐based thresholding parameters to identify plausible micronodules. The FP reduction module employs a 3D convolutional neural network (CNN) to classify each identified candidate. It automatically encodes the discriminative representations by exploiting the volumetric information of each candidate. A set of 872 micro‐nodules in 598 CT scans marked by at least two radiologists are extracted from the Lung Image Database Consortium and Image Database Resource Initiative to test our CAD scheme. The CAD scheme achieves a detection sensitivity of 86.7% (756/872) with only 8 FPs/scan and an AUC of 0.98. Our proposed CAD scheme efficiently identifies micronodules in thoracic scans with only a small number of FPs. Our experimental results provide evidence that the automatically generated features by the 3D CNN are highly discriminant, thus making it a well‐suited FP reduction module of a CAD scheme.

[1]  M. Kenward,et al.  An Introduction to the Bootstrap , 2007 .

[2]  Ronald M. Summers,et al.  A New 2.5D Representation for Lymph Node Detection Using Random Sets of Deep Convolutional Neural Network Observations , 2014, MICCAI.

[3]  Elizabeth E Ward,et al.  Silica: A lung carcinogen , 2014, CA: a cancer journal for clinicians.

[4]  David A Lynch,et al.  State of the art: Imaging of occupational lung disease. , 2014, Radiology.

[5]  D. Shen,et al.  Computer-Aided Diagnosis with Deep Learning Architecture: Applications to Breast Lesions in US Images and Pulmonary Nodules in CT Scans , 2016, Scientific Reports.

[6]  Ola Spjuth,et al.  Deep Learning in Image Cytometry: A Review , 2018, Cytometry. Part A : the journal of the International Society for Analytical Cytology.

[7]  Max A. Viergever,et al.  On Combining Computer-Aided Detection Systems , 2011, IEEE Transactions on Medical Imaging.

[8]  Bram van Ginneken,et al.  Pulmonary Nodule Detection in CT Images: False Positive Reduction Using Multi-View Convolutional Networks , 2016, IEEE Transactions on Medical Imaging.

[9]  N. Müller,et al.  Fleischner Society: glossary of terms for thoracic imaging. , 2008, Radiology.

[10]  Piergiorgio Cerello,et al.  A novel multithreshold method for nodule detection in lung CT. , 2009, Medical physics.

[11]  Hao Chen,et al.  Multilevel Contextual 3-D CNNs for False Positive Reduction in Pulmonary Nodule Detection , 2017, IEEE Transactions on Biomedical Engineering.

[12]  Rafael Wiemker,et al.  Performance analysis for computer-aided lung nodule detection on LIDC data , 2007, SPIE Medical Imaging.

[13]  Nitish Srivastava,et al.  Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..

[14]  D. Xu,et al.  Nodule management protocol of the NELSON randomised lung cancer screening trial. , 2006, Lung cancer.

[15]  M. McNitt-Gray,et al.  Lung micronodules: automated method for detection at thin-section CT--initial experience. , 2003, Radiology.

[16]  Sergey Ioffe,et al.  Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.

[17]  Bram van Ginneken,et al.  Automated detection and quantification of micronodules in thoracic CT scans to identify subjects at risk for silicosis , 2014, Medical Imaging.

[18]  L. Schwartz,et al.  Automatic detection of small lung nodules on CT utilizing a local density maximum algorithm , 2003, Journal of applied clinical medical physics.

[19]  Yannick Le Moullec,et al.  Methods for increased sensitivity and scope in automatic segmentation and detection of lung nodules in CT images , 2015, 2015 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT).

[20]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[21]  Vivek Vaidya,et al.  Lung nodule detection in CT using 3D convolutional neural networks , 2017, 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017).

[22]  J Wardle,et al.  Pulmonary nodules and CT screening: the past, present and future , 2016, Thorax.

[23]  Shouliang Qi,et al.  CNN models discriminating between pulmonary micro-nodules and non-nodules from CT images , 2018, Biomedical engineering online.

[24]  Yannick Le Moullec,et al.  Unsupervised Feature Mapping via Stacked Sparse Autoencoder for Automated Detection of Large Pulmonary nodules in CT Images , 2017 .

[25]  M. Azharuddin,et al.  Evaluating pulmonary nodules to detect lung cancer: Does Fleischner criteria really work? , 2017 .

[26]  Jan Cornelis,et al.  A novel computer-aided lung nodule detection system for CT images. , 2011, Medical physics.

[27]  C. Park,et al.  Imaging of occupational lung disease. , 2001, Radiographics : a review publication of the Radiological Society of North America, Inc.

[28]  Yoshua Bengio,et al.  Understanding the difficulty of training deep feedforward neural networks , 2010, AISTATS.

[29]  Temesguen Messay,et al.  A new computationally efficient CAD system for pulmonary nodule detection in CT imagery , 2010, Medical Image Anal..

[30]  C Peroni,et al.  Large scale validation of the M5L lung CAD on heterogeneous CT datasets. , 2015, Medical physics.

[31]  D. P. Chakraborty,et al.  A status report on free-response analysis. , 2010, Radiation protection dosimetry.

[32]  Lubomir M. Hadjiiski,et al.  Computer-aided detection of lung nodules: false positive reduction using a 3D gradient field method and 3D ellipsoid fitting. , 2005, Medical physics.

[33]  Richard C. Pais,et al.  The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): a completed reference database of lung nodules on CT scans. , 2011, Medical physics.

[34]  John A. Curtis,et al.  Silicosis: a review. , 2007, Disease-a-month : DM.

[35]  Klinikum Westfalen,et al.  OCCUPATIONAL SAFETY AND HEALTH SERIES No . 22 ( Rev . ) GUIDELINES FOR THE USE OF ILO INTERNATIONAL CLASSIFICATION OF RADIOGRAPHS OF PNEUMOCONIOSES , 2009 .

[36]  Yannick Le Moullec,et al.  Automatic detection of multisize pulmonary nodules in CT images: Large‐scale validation of the false‐positive reduction step , 2018, Medical physics.

[37]  Qi Dou.  Multi-level Contextual 3D CNNs for False Positive Reduction in Pulmonary Nodule Detection , 2017 .