Lung tissue characterization for emphysema differential diagnosis using deep convolutional neural networks

In this study, we propose and validate an end-to-end pipeline based on deep learning for differential diagnosis of emphysema in thoracic CT images. The five lung tissue patterns involved in most differential restrictive and obstructive lung disease diagnoses include: emphysema, ground glass, fibrosis, micronodule, and normal. Four established network architectures have been trained and evaluated. To the best of our knowledge, this is the first comprehensive end-to-end deep CNN pipeline for differential diagnosis of emphysema. A comparative analysis shows the performance of the proposed models on two publicly available datasets.

[1]  Antoine Geissbühler,et al.  Building a reference multimedia database for interstitial lung diseases , 2012, Comput. Medical Imaging Graph..

[2]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.

[3]  R. Spillane,et al.  High-resolution CT of the lungs. , 1993, American family physician.

[4]  David Dagan Feng,et al.  Feature-Based Image Patch Approximation for Lung Tissue Classification , 2013, IEEE Transactions on Medical Imaging.

[5]  Lauge Sørensen,et al.  Quantitative Analysis of Pulmonary Emphysema Using Local Binary Patterns , 2010, IEEE Transactions on Medical Imaging.

[6]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[7]  P ? ? ? ? ? ? ? % ? ? ? ? , 1991 .

[8]  Bram van Ginneken,et al.  Computer analysis of computed tomography scans of the lung: a survey , 2006, IEEE Transactions on Medical Imaging.

[9]  B. van Ginneken,et al.  Computer-aided diagnosis in high resolution CT of the lungs. , 2003, Medical physics.

[10]  Yulia Arzhaeva,et al.  Computer-aided detection of interstitial abnormalities in chest radiographs using a reference standard based on computed tomography. , 2007, Medical physics.

[11]  Ronald M. Summers,et al.  Holistic classification of CT attenuation patterns for interstitial lung diseases via deep convolutional neural networks , 2018, Comput. methods Biomech. Biomed. Eng. Imaging Vis..

[12]  E. Hoffman,et al.  Computer recognition of regional lung disease patterns. , 1999, American journal of respiratory and critical care medicine.

[14]  Tsuyoshi Murata,et al.  {m , 1934, ACML.

[15]  David Beymer,et al.  Automated volumetric lung segmentation of thoracic CT images using fully convolutional neural network , 2018, Medical Imaging.

[16]  Dimitri Van De Ville,et al.  Near-Affine-Invariant Texture Learning for Lung Tissue Analysis Using Isotropic Wavelet Frames , 2012, IEEE Transactions on Information Technology in Biomedicine.

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

[18]  Lena Costaridou,et al.  Texture-Based Identification and Characterization of Interstitial Pneumonia Patterns in Lung Multidetector CT , 2010, IEEE Transactions on Information Technology in Biomedicine.

[19]  S. Brereton,et al.  A Diagnosis , 1875, The Indian medical gazette.

[20]  Marios Anthimopoulos,et al.  Lung Pattern Classification for Interstitial Lung Diseases Using a Deep Convolutional Neural Network , 2016, IEEE Transactions on Medical Imaging.

[21]  Danna Zhou,et al.  d. , 1934, Microbial pathogenesis.