MRI Cross-Modality NeuroImage-to-NeuroImage Translation

TL;DR

This paper introduces a deep learning framework using cGANs for generating missing MRI modalities from existing ones, enhancing cross-modality registration and segmentation without additional data, with promising results on brain MRI datasets.

Contribution

The paper proposes a novel N2N translation framework that jointly exploits low-level and high-level features for cross-modality MRI synthesis and improves clinical tasks like registration and segmentation.

Findings

Outperforms state-of-the-art on five brain MRI datasets

Improves cross-modality registration accuracy

Enhances segmentation performance using translated modalities

Abstract

We present a cross-modality generation framework that learns to generate translated modalities from given modalities in MR images without real acquisition. Our proposed method performs NeuroImage-to-NeuroImage translation (abbreviated as N2N) by means of a deep learning model that leverages conditional generative adversarial networks (cGANs). Our framework jointly exploits the low-level features (pixel-wise information) and high-level representations (e.g. brain tumors, brain structure like gray matter, etc.) between cross modalities which are important for resolving the challenging complexity in brain structures. Our framework can serve as an auxiliary method in clinical diagnosis and has great application potential. Based on our proposed framework, we first propose a method for cross-modality registration by fusing the deformation fields to adopt the cross-modality information from translated modalities. Second, we propose an approach for MRI segmentation, translated multichannel segmentation (TMS), where given modalities, along with translated modalities, are segmented by fully convolutional networks (FCN) in a multichannel manner. Both of these two methods successfully adopt the cross-modality information to improve the performance without adding any extra data. Experiments demonstrate that our proposed framework advances the state-of-the-art on five brain MRI datasets. We also observe encouraging results in cross-modality registration and segmentation on some widely adopted brain datasets. Overall, our work can serve as an auxiliary method in clinical diagnosis and be applied to various tasks in medical fields. Keywords: image-to-image, cross-modality, registration, segmentation, brain MRI