Detail publikace

Superresolution of MRI brain images using unbalanced 3D Dense-U-Net network

Originální název

Superresolution of MRI brain images using unbalanced 3D Dense-U-Net network

Anglický název

Superresolution of MRI brain images using unbalanced 3D Dense-U-Net network

Jazyk

en

Originální abstrakt

This paper proposes an unbalanced end-to-end trained 3D Dense-U-Net network for brain MRI images superresolution. We evaluated capabilites of the proposed architecture on upsampling the MRI brain scans in the factor of 2, 4 and 8 and compared the results with resampled images using lanczos, spline and bilinear interpolation achieving best results. While the network does not exceed superresolution capabilites of state-of-the-art GAN networks, it does not require large dataset, is easy to train and capable of processing 3D images in resolution suitable for medical image processing.

Anglický abstrakt

This paper proposes an unbalanced end-to-end trained 3D Dense-U-Net network for brain MRI images superresolution. We evaluated capabilites of the proposed architecture on upsampling the MRI brain scans in the factor of 2, 4 and 8 and compared the results with resampled images using lanczos, spline and bilinear interpolation achieving best results. While the network does not exceed superresolution capabilites of state-of-the-art GAN networks, it does not require large dataset, is easy to train and capable of processing 3D images in resolution suitable for medical image processing.

BibTex


@inproceedings{BUT157997,
  author="Martin {Kolařík} and Radim {Burget} and Václav {Uher} and Lukáš {Povoda}",
  title="Superresolution of MRI brain images using unbalanced 3D Dense-U-Net network",
  annote="This paper proposes an unbalanced end-to-end trained 3D Dense-U-Net network for brain MRI images superresolution. We evaluated capabilites of the proposed architecture on upsampling the MRI brain scans in the factor of 2, 4 and 8 and compared the results with resampled images using lanczos, spline and bilinear interpolation achieving best results. While the network does not exceed superresolution capabilites of state-of-the-art GAN networks, it does not require large dataset, is easy to train and capable of processing 3D images in resolution suitable for medical image processing.",
  booktitle="2019 42nd International Conference on Telecommunications and Signal Processing (TSP)",
  chapter="157997",
  doi="10.1109/TSP.2019.8768829",
  howpublished="online",
  year="2019",
  month="july",
  pages="643--646",
  type="conference paper"
}