SynNet: Structure-Preserving Fully Convolutional Networks for Medical Image Synthesis

TL;DR

This paper introduces SynNet, a fully convolutional neural network with a custom loss function designed for accurate cross-modal medical image synthesis, validated on the BRATS dataset against existing methods.

Contribution

The paper presents SynNet, a novel structure-preserving deep learning architecture with a custom loss function for improved multi-modal medical image synthesis.

Findings

SynNet outperforms three state-of-the-art methods on BRATS dataset.

The custom loss function enhances synthesis accuracy.

SynNet effectively handles various input-output configurations.

Abstract

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.