RSNA 2017

Deep Learning-Based Content-Based Image Retrieval for Finding HRCT Images of Similar Patients with Interstitial Lung Disease: Validation with 100 Paired HRCTs and Automatic Quantification of Six Disease Patterns

  • Nov. 2017
  • by Kyu-Hwan Jung et. al.

Interstitial lung diseases (ILDs) represent a major cause of morbidity and mortality. High-resolution computed tomography (HRCT) has become critical to characterize the imaging patterns of ILD, but this approach remains vulnerable to inter- and intra-observer variation. To overcome human variation, automated techniques have been applied for differentiating a variety of obstructive lung diseases based on the features of a density histogram and texture analyses[1-8]. Quantitative assessment of lung parenchymal texture is important to analyze and differentiate regional diseased patterns of ILD, which would lead to content based image retrieval (CBIR). Using deep learning technique with Siamese convolutional neural net (CNN) on raw image itself and classified disease patterns, 3D CBIR at HRCT is potentially useful for diagnosis and decision by retrieval of similar HRCT to referring similar patient previously diagnosed with known treatment response and survival. To address these unmet clinical needs, we have developed DILD CBIR platform, a deep learning-based CBIR system and its evaluation tool with known 100 paired HRCTs of same patient; thus to provide an efficient and reliable quantification for the assessment of CBIR performance for ILD patients.


Kyu-Hwan Jung, Ilji Choi, Sangkeun Kim, Namkug Kim, Beomhee Park, Eunsol Lee, Sangmin Lee, Joon Beom Seo

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