Deep learning networks with p-norm loss layers for spatial resolution enhancement of 3D medical images
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Autores: Karl Thurnhofer Hemsi, Ezequiel López Rubio
, Nuria Roe Vellve , Miguel A. Molina Cabello
- Localización: From Bioinspired Systems and Biomedical Applications to Machine Learning: 8th International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2019, Almería, Spain, June 3–7, 2019, Proceedings, Part II / coord. por Hojjat Adeli; José Manuel Ferrández Vicente (dir. congr.) , José Ramón Álvarez Sánchez (dir. congr.) , Félix de la Paz López (dir. congr.) , Francisco Javier Toledo Moreo (dir. congr.), 2019, ISBN 978-3-030-19651-6, págs. 287-296
- Idioma: inglés
- Texto completo no disponible (Saber más ...)
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Resumen
Nowadays, obtaining high-quality magnetic resonance (MR)images is a complex problem due to several acquisition factors, but is crucial in order to perform good diagnostics. The enhancement of the resolutionis a typical procedure applied after the image generation. Stateof-the-art works gather a large variety of methods for super-resolution(SR), among which deep learning has become very popular during the last years. Most of the SR deep-learning methods are based on the minimizationof the residuals by the use of Euclidean loss layers. In thispaper, we propose an SR model based on the use of a p-norm loss layer to improve the learning process and obtain a better high-resolution (HR)image. This method was implemented using a three-dimensional convolutional neural network (CNN), and tested for several norms in order to determine the most robust fit. The proposed methodology was trained and tested with sets of MR structural T1-weighted images and showed better outcomes quantitatively, in terms of Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM), and the restored and the calculated residual images showed better CNN outputs
