Building a reference multimedia database for interstitial lung diseases

This paper describes the methodology used to create a multimedia collection of cases with interstitial lung diseases (ILDs) at the University Hospitals of Geneva. The dataset contains high-resolution computed tomography (HRCT) image series with three-dimensional annotated regions of pathological lung tissue along with clinical parameters from patients with pathologically proven diagnoses of ILDs. The motivations for this work is to palliate the lack of publicly available collections of ILD cases to serve as a basis for the development and evaluation of image-based computerized diagnostic aid. After 38 months of data collection, the library contains 128 patients affected with one of the 13 histological diagnoses of ILDs, 108 image series with more than 41l of annotated lung tissue patterns as well as a comprehensive set of 99 clinical parameters related to ILDs. The database is available for research on request and after signature of a license agreement.

[1]  Alejandro F. Frangi,et al.  @neurIST - Towards a System Architecture for Advanced Disease Management through Integration of Heterogeneous Data, Computing, and Complex Processing Services , 2008, 2008 21st IEEE International Symposium on Computer-Based Medical Systems.

[2]  G Lutterbey,et al.  Initial experience with lung-MRI at 3.0T: Comparison with CT and clinical data in the evaluation of interstitial lung disease activity. , 2007, European journal of radiology.

[3]  Zaid J. Towfic,et al.  The Lung Image Database Consortium (LIDC) data collection process for nodule detection and annotation , 2007, SPIE Medical Imaging.

[4]  Richard A. Robb,et al.  Processing of CT images for analysis of diffuse lung disease in the lung tissue research consortium , 2008, SPIE Medical Imaging.

[5]  S. Worthy.  High resolution computed tomography of the lungs , 1995, BMJ.

[6]  Alexander Horsch,et al.  Establishing an International Reference Image Database for Research and Development in Medical Image Processing , 2004, Methods of Information in Medicine.

[7]  G F Cooper,et al.  Building a medical multimedia database system to integrate clinical information: an application of high-performance computing and communications technology. , 1995, Bulletin of the Medical Library Association.

[8]  E. Hoffman,et al.  Lung image database consortium: developing a resource for the medical imaging research community. , 2004, Radiology.

[9]  R. Robb,et al.  The Lung Tissue Research Consortium: An extensive open database containing histological, clinical, and radiological data to study chronic lung disease , 2006, The Insight Journal.

[10]  J F Wade,et al.  Infiltrative and interstitial lung disease in the elderly patient. , 1993, Clinics in chest medicine.

[11]  Phil Brodatz,et al.  Textures: A Photographic Album for Artists and Designers , 1966 .

[12]  N. Müller,et al.  High-Resolution CT of the Lung , 1997 .

[13]  K. Doi,et al.  Current status and future potential of computer-aided diagnosis in medical imaging. , 2005, The British journal of radiology.

[14]  Susan Murray,et al.  Idiopathic interstitial pneumonia: what is the effect of a multidisciplinary approach to diagnosis? , 2004, American journal of respiratory and critical care medicine.

[15]  Henning Müller,et al.  A reference data set for the evaluation of medical image retrieval systems. , 2004, Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society.

[16]  L E Feinendegen,et al.  Evidence for beneficial low level radiation effects and radiation hormesis. , 2005, The British journal of radiology.

[17]  Antoine Geissbühler,et al.  Case-based lung image categorization and retrieval for interstitial lung diseases: clinical workflows , 2011, International Journal of Computer Assisted Radiology and Surgery.

[18]  Adrien Depeursinge,et al.  Affine-invariant texture analysis and retrieval of 3D medical images with clinical context integration , 2010 .

[19]  Kunio Doi,et al.  Computer-aided diagnosis in medical imaging: Historical review, current status and future potential , 2007, Comput. Medical Imaging Graph..

[20]  Antoine Geissbühler,et al.  3D Case-Based Retrieval for Interstitial Lung Diseases , 2009, MCBR-CDS.

[21]  N. Müller,et al.  Idiopathic pulmonary fibrosis: spectrum of high-resolution CT findings. , 2005, AJR. American journal of roentgenology.

[22]  H. Ohmatsu,et al.  Detection failures in spiral CT screening for lung cancer: analysis of CT findings. , 1999, Radiology.

[23]  Grant Berges,et al.  Approach to ground-glass opacification of the lung. , 2002, Seminars in ultrasound, CT, and MR.

[24]  V. Poletti,et al.  Comparison of registries of interstitial lung diseases in three European countries. , 2001, The European respiratory journal. Supplement.

[25]  Kunio Doi,et al.  Computer-aided diagnosis in chest radiography , 2007, Comput. Medical Imaging Graph..

[26]  Christian Lovis,et al.  Lung CT Analysis and Retrieval as a Diagnostic Aid , 2005, MIE.

[27]  Arcot Sowmya,et al.  LMIK - learning medical image knowledge: an Internet-based medical image knowledge acquisition framework , 2003, IS&T/SPIE Electronic Imaging.

[28]  H. Muller,et al.  Lung Tissue Classification Using Wavelet Frames , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[29]  Timothy M. Franz,et al.  Enhancement of clinicians' diagnostic reasoning by computer-based consultation: a multisite study of 2 systems. , 1999, JAMA.

[30]  Henning Müller,et al.  Strategies for health data exchange for secondary, cross-institutional clinical research , 2010, Comput. Methods Programs Biomed..

[31]  Antoine Geissbühler,et al.  A Review of Content{Based Image Retrieval Systems in Medical Applications { Clinical Bene(cid:12)ts and Future Directions , 2022 .

[32]  Adrien Depeursinge,et al.  A framework for diagnosis of interstitial lung diseases in HRCT: the TALISMAN project , 2008 .

[33]  Geoffrey McLennan,et al.  The Lung Image Database Consortium (LIDC): an evaluation of radiologist variability in the identification of lung nodules on CT scans. , 2007, Academic radiology.

[34]  A. Kak,et al.  Automated storage and retrieval of thin-section CT images to assist diagnosis: system description and preliminary assessment. , 2003, Radiology.

[35]  A. Nicholson,et al.  HRCT diagnosis of diffuse parenchymal lung disease: inter-observer variation , 2004, Thorax.

[36]  Henning Müller,et al.  3D lung image retrieval using localized features , 2011, Medical Imaging.

[37]  Henning Müller,et al.  Fusing visual and clinical information for lung tissue classification in high-resolution computed tomography , 2010, Artif. Intell. Medicine.

[38]  Koichiro Tatsumi,et al.  Impact of interstitial lung disease on surgical morbidity and mortality for lung cancer: analyses of short-term and long-term outcomes. , 2003, The Journal of thoracic and cardiovascular surgery.

[39]  N L Müller,et al.  Clinical value of high-resolution CT in chronic diffuse lung disease. , 1991, AJR. American journal of roentgenology.

[40]  A. P. Reeves,et al.  A public image database to support research in computer aided diagnosis , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.