SynNet: Structure-Preserving Fully Convolutional Networks for Medical Image Synthesis
This work addresses the need for more accurate synthetic medical images for applications like radiation therapy planning, but it appears incremental as it builds on existing deep learning methods with a custom loss function.
The paper tackles the problem of inaccurate cross-modal medical image synthesis by proposing SynNet, a fully convolutional deep learning architecture with a structure-preserving custom loss function, achieving improved results validated on the BRATS dataset against three state-of-the-art methods.
Cross modal image syntheses is gaining significant interests for its ability to estimate target images of a different modality from a given set of source images,like estimating MR to MR, MR to CT, CT to PET etc, without the need for an actual acquisition.Though they show potential for applications in radiation therapy planning,image super resolution, atlas construction, image segmentation etc.The synthesis results are not as accurate as the actual acquisition.In this paper,we address the problem of multi modal image synthesis by proposing a fully convolutional deep learning architecture called the SynNet.We extend the proposed architecture for various input output configurations. And finally, we propose a structure preserving custom loss function for cross-modal image synthesis.We validate the proposed SynNet and its extended framework on BRATS dataset with comparisons against three state-of-the art methods.And the results of the proposed custom loss function is validated against the traditional loss function used by the state-of-the-art methods for cross modal image synthesis.