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13 March 2019 Similar CT image retrieval method based on lesion nature and its three-dimensional distribution
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Abstract
In imaging diagnosis, radiologists refer to the CT images of the similar cases. However, it is a big burden for them to search such CT images from the huge numbers of CT images. To solve this problem, many retrieval methods of CT images have been developed. Most existing retrieval methods target cases in which lesions exist within a limited region of the lung. These methods retrieve similar cases by calculating the similarity to the region specified on a slice image of the query case, for example, solitary pulmonary nodules. However, radiologists diagnose not only such cases but also diffuse lung disease (DLD), where lesions exist throughout the lung. Radiologists diagnose DLDs by grasping the threedimensional (3D) distribution of lesion textures. However, the existing methods cannot retrieve similar DLDs. We propose a novel method that can retrieve morphologically similar cases based on the radiologist’s knowledge, how they diagnose DLDs. In the proposed method, we configure a 3D model for the central-peripheral region of a lung, represent the similarity for the 3D distribution of lesions as histograms, and then retrieve the cases of the similar histograms. We evaluate the average precision of the proposed method for DLD CT images. For the top 5 cases, the mean of the average precisions of the proposed method is 0.84 and is better than that of the method that only calculates the volume rate of the lesions in the lung (0.64). The proposed method retrieves similar DLDs based on 3D distribution of lesion textures and is expected to contribute to diagnosis support in clinical practice.
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Yasutaka Moriwaki, Nobuhiro Miyazaki, Hiroaki Takebe, Takayuki Baba, Hiroaki Terada, Toru Higaki, Kazuo Awai, Machiko Nakagawa, Akio Ozawa, Kennji Kitayama, and Yasuharu Ogino "Similar CT image retrieval method based on lesion nature and its three-dimensional distribution", Proc. SPIE 10950, Medical Imaging 2019: Computer-Aided Diagnosis, 109503T (13 March 2019); https://doi.org/10.1117/12.2506322
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