Lung lesion detection in FDG-PET/CT with Gaussian process regression

Ryosuke Kamesawa; Issei Sato; Shouhei Hanaoka; Yukihiro Nomura; Mitsutaka Nemoto; Naoto Hayashi; Masashi Sugiyama

doi:10.1117/12.2255588

3 March 2017 Lung lesion detection in FDG-PET/CT with Gaussian process regression

Ryosuke Kamesawa, Issei Sato, Shouhei Hanaoka, Yukihiro Nomura, Mitsutaka Nemoto, Naoto Hayashi, Masashi Sugiyama

Proceedings Volume 10134, Medical Imaging 2017: Computer-Aided Diagnosis; 101340C (2017) https://doi.org/10.1117/12.2255588
Event: SPIE Medical Imaging, 2017, Orlando, Florida, United States

Abstract

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%.

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Citation Download Citation

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 (3 March 2017); https://doi.org/10.1117/12.2255588

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