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Classification of parotid gland tumors by using multimodal MRI and deep learning.
Chang, Yi-Ju; Huang, Teng-Yi; Liu, Yi-Jui; Chung, Hsiao-Wen; Juan, Chun-Jung.
Afiliación
  • Chang YJ; Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan.
  • Huang TY; Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan.
  • Liu YJ; Department of Automatic Control Engineering, Feng Chia University, Taichung, Taiwan.
  • Chung HW; Department of Electrical Engineering, National Taiwan University, Taipei, Taiwan.
  • Juan CJ; Department of Medical Imaging, China Medical University Hsinchu Hospital, Hsinchu, Taiwan.
NMR Biomed ; 34(1): e4408, 2021 01.
Article en En | MEDLINE | ID: mdl-32886955
Various MRI sequences have shown their potential to discriminate parotid gland tumors, including but not limited to T2 -weighted, postcontrast T1 -weighted, and diffusion-weighted images. In this study, we present a fully automatic system for the diagnosis of parotid gland tumors by using deep learning methods trained on multimodal MRI images. We used a two-dimensional convolution neural network, U-Net, to segment and classify parotid gland tumors. The U-Net model was trained with transfer learning, and a specific design of the batch distribution optimized the model accuracy. We also selected five combinations of MRI contrasts as the input data of the neural network and compared the classification accuracy of parotid gland tumors. The results indicated that the deep learning model with diffusion-related parameters performed better than those with structural MR images. The performance results (n = 85) of the diffusion-based model were as follows: accuracy of 0.81, 0.76, and 0.71, sensitivity of 0.83, 0.63, and 0.33, and specificity of 0.80, 0.84, and 0.87 for Warthin tumors, pleomorphic adenomas, and malignant tumors, respectively. Combining diffusion-weighted and contrast-enhanced T1 -weighted images did not improve the prediction accuracy. In summary, the proposed deep learning model could classify Warthin tumor and pleomorphic adenoma tumor but not malignant tumor.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Glándula Parótida / Neoplasias de la Parótida / Imagen por Resonancia Magnética / Aprendizaje Profundo Tipo de estudio: Prognostic_studies Límite: Female / Humans / Male / Middle aged Idioma: En Revista: NMR Biomed Asunto de la revista: DIAGNOSTICO POR IMAGEM / MEDICINA NUCLEAR Año: 2021 Tipo del documento: Article País de afiliación: Taiwán Pais de publicación: Reino Unido
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Glándula Parótida / Neoplasias de la Parótida / Imagen por Resonancia Magnética / Aprendizaje Profundo Tipo de estudio: Prognostic_studies Límite: Female / Humans / Male / Middle aged Idioma: En Revista: NMR Biomed Asunto de la revista: DIAGNOSTICO POR IMAGEM / MEDICINA NUCLEAR Año: 2021 Tipo del documento: Article País de afiliación: Taiwán Pais de publicación: Reino Unido