Pulmonary Nodule Detection in CT Images: False Positive Reduction Using Multi-View Convolutional Networks

We propose a novel Computer-Aided Detection (CAD) system for pulmonary nodules using multi-view convolutional networks (ConvNets), for which discriminative features are automatically learnt from the training data. The network is fed with nodule candidates obtained by combining three candidate detectors specifically designed for solid, subsolid, and large nodules. For each candidate, a set of 2-D patches from differently oriented planes is extracted. The proposed architecture comprises multiple streams of 2-D ConvNets, for which the outputs are combined using a dedicated fusion method to get the final classification. Data augmentation and dropout are applied to avoid overfitting. On 888 scans of the publicly available LIDC-IDRI dataset, our method reaches high detection sensitivities of 85.4% and 90.1% at 1 and 4 false positives per scan, respectively. An additional evaluation on independent datasets from the ANODE09 challenge and DLCST is performed. We showed that the proposed multi-view ConvNets is highly suited to be used for false positive reduction of a CAD system.

[1]  Bram van Ginneken,et al.  Off-the-shelf convolutional neural network features for pulmonary nodule detection in computed tomography scans , 2015, 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI).

[2]  Jürgen Schmidhuber,et al.  Multi-column deep neural network for traffic sign classification , 2012, Neural Networks.

[3]  Jan Cornelis,et al.  Phased searching with NEAT in a Time-Scaled Framework: Experiments on a computer-aided detection system for lung nodules , 2013, Artif. Intell. Medicine.

[4]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[5]  Robert Tibshirani,et al.  An Introduction to the Bootstrap , 1994 .

[6]  Youngjin Yoo,et al.  Modeling the Variability in Brain Morphology and Lesion Distribution in Multiple Sclerosis by Deep Learning , 2014, MICCAI.

[7]  Huai Li,et al.  Artificial convolution neural network for medical image pattern recognition , 1995, Neural Networks.

[8]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[9]  Eran A Barnoy,et al.  Toward clinically usable CAD for lung cancer screening with computed tomography , 2014, European Radiology.

[10]  Ilaria Gori,et al.  A voxel-based neural approach (VBNA) to identify lung nodules in the ANODE09 study , 2009, Medical Imaging.

[11]  Fabio A. González,et al.  A Deep Learning Architecture for Image Representation, Visual Interpretability and Automated Basal-Cell Carcinoma Cancer Detection , 2013, MICCAI.

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

[13]  Tae-Sun Choi,et al.  Automated Pulmonary Nodule Detection System in Computed Tomography Images: A Hierarchical Block Classification Approach , 2013, Entropy.

[14]  A. Dirksen,et al.  The Danish Randomized Lung Cancer CT Screening Trial—Overall Design and Results of the Prevalence Round , 2009, Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer.

[15]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.

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

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

[18]  Ricardo A. M. Valentim,et al.  Computer-aided detection system for lung cancer in computed tomography scans: Review and future prospects , 2014, BioMedical Engineering OnLine.

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

[20]  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.

[21]  Hiroshi Fujita,et al.  Fast lung nodule detection in chest CT images using cylindrical nodule-enhancement filter , 2012, International Journal of Computer Assisted Radiology and Surgery.

[22]  Colin Jacobs,et al.  Predictive Accuracy of the PanCan Lung Cancer Risk Prediction Model -External Validation based on CT from the Danish Lung Cancer Screening Trial , 2015, European Radiology.

[23]  Dinggang Shen,et al.  Deep Learning Based Imaging Data Completion for Improved Brain Disease Diagnosis , 2014, MICCAI.

[24]  Bram van Ginneken,et al.  A large-scale evaluation of automatic pulmonary nodule detection in chest CT using local image features and k-nearest-neighbour classification , 2009, Medical Image Anal..

[25]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[26]  Donato Cascio,et al.  Automatic detection of lung nodules in CT datasets based on stable 3D mass-spring models , 2012, Comput. Biol. Medicine.

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

[28]  J. Mayo,et al.  The Lung Reporting and Data System (LU-RADS): A Proposal for Computed Tomography Screening , 2014, Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes.

[29]  Luca Bogoni,et al.  Performance of computer-aided detection of pulmonary nodules in low-dose CT , 2018 .

[30]  M. Roizen.  Reduced Lung-Cancer Mortality with Low-Dose Computed Tomographic Screening , 2012 .

[31]  Christian Igel,et al.  Deep Feature Learning for Knee Cartilage Segmentation Using a Triplanar Convolutional Neural Network , 2013, MICCAI.

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

[33]  Fei-Fei Li,et al.  Large-Scale Video Classification with Convolutional Neural Networks , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[34]  Bram van Ginneken,et al.  Automatic detection of subsolid pulmonary nodules in thoracic computed tomography images , 2014, Medical Image Anal..

[35]  Razvan Pascanu,et al.  Theano: new features and speed improvements , 2012, ArXiv.

[36]  Andrew Zisserman,et al.  Return of the Devil in the Details: Delving Deep into Convolutional Nets , 2014, BMVC.

[37]  Qiang Li,et al.  High performance lung nodule detection schemes in CT using local and global information. , 2012, Medical physics.

[38]  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.

[39]  Ilaria Gori,et al.  Comparing and combining algorithms for computer-aided detection of pulmonary nodules in computed tomography scans: The ANODE09 study , 2010, Medical Image Anal..

[40]  A. Reeves,et al.  Automated detection of small pulmonary nodules in whole lung CT scans. , 2007, Academic radiology.

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

[42]  Heinz-Otto Peitgen,et al.  Morphological segmentation and partial volume analysis for volumetry of solid pulmonary lesions in thoracic CT scans , 2006, IEEE Transactions on Medical Imaging.

[43]  Bram van Ginneken,et al.  Automatic detection of large pulmonary solid nodules in thoracic CT images. , 2015, Medical physics.