In this study, we propose a novel method of lung lesion detection in FDG-PET/CT volumes without labeling lesions. In our method, the probability distribution over normal standardized uptake values (SUVs) is estimated from the features extracted from the corresponding volume of interest (VOI) in the CT volume, which include gradient-based and texture-based features. To estimate the distribution, we use Gaussian process regression with an automatic relevance determination kernel, which provides the relevance of feature values to estimation. Our model was trained using FDG-PET/CT volumes of 121 normal cases. In the lesion detection phase, the actual SUV is judged as normal or abnormal by comparison with the estimated SUV distribution. According to the validation using 28 FDG-PET/CT volumes with 34 lung lesions, the sensitivity of the proposed method at 5.0 false positives per case was 81.9%.
Ryosuke Kamesawa, Issei Sato, Shouhei Hanaoka, Yukihiro Nomura, Mitsutaka Nemoto, Naoto Hayashi, and Masashi Sugiyama, "Lung lesion detection in FDG-PET/CT with Gaussian process regression," Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 101340C (Presented at SPIE Medical Imaging: February 13, 2017; Published: 3 March 2017); https://doi.org/10.1117/12.2255588.
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