Multisequence Magnetic Resonance Imaging (MRI) provides a reliable diagnosis
in clinical applications through complementary information within sequences.
However, in practice, the absence of certain MR sequences is a common problem
that can lead to inconsistent analysis results. In this work, we propose a
novel unified framework for synthesizing multisequence MR images, called Hybrid
Fusion GAN (HF-GAN). We introduce a hybrid fusion encoder designed to ensure
the disentangled extraction of complementary and modality-specific information,
along with a channel attention-based feature fusion module that integrates the
features into a common latent space handling the complexity from combinations
of accessible MR sequences. Common feature representations are transformed into
a target latent space via the modality infuser to synthesize missing MR
sequences. We have performed experiments on multisequence brain MRI datasets
from healthy individuals and patients diagnosed with brain tumors. Experimental
results show that our method outperforms state-of-the-art methods in both
quantitative and qualitative comparisons. In addition, a detailed analysis of
our framework demonstrates the superiority of our designed modules and their
effectiveness for use in data imputation tasks.

提出了一种名为混合融合生成对抗网络（HF-GAN）的新型统一框架，通过多模式磁共振成像，使得通过补充信息来提供可靠的临床应用诊断。实验证明，该方法在数据插补任务中表现出优异的性能，并且比现有方法在定量和定性比较方面更加优越。

基于混合融合的多序列脑 MRI 综合合成统一框架

A Unified Framework for Synthesizing Multisequence Brain MRI via Hybrid  Fusion

We present an efficient deep learning approach for the challenging task of
tumor segmentation in multisequence MR images. In recent years, Convolutional
Neural Networks (CNN) have achieved state-of-the-art performances in a large
variety of recognition tasks in medical imaging. Because of the considerable
computational cost of CNNs, large volumes such as MRI are typically processed
by subvolumes, for instance slices (axial, coronal, sagittal) or small 3D
patches. In this paper we introduce a CNN-based model which efficiently
combines the advantages of the short-range 3D context and the long-range 2D
context. To overcome the limitations of specific choices of neural network
architectures, we also propose to merge outputs of several cascaded 2D-3D
models by a voxelwise voting strategy. Furthermore, we propose a network
architecture in which the different MR sequences are processed by separate
subnetworks in order to be more robust to the problem of missing MR sequences.
Finally, a simple and efficient algorithm for training large CNN models is
introduced. We evaluate our method on the public benchmark of the BRATS 2017
challenge on the task of multiclass segmentation of malignant brain tumors. Our
method achieves good performances and produces accurate segmentations with
median Dice scores of 0.918 (whole tumor), 0.883 (tumor core) and 0.854
(enhancing core). Our approach can be naturally applied to various tasks
involving segmentation of lesions or organs.

提出了一种基于卷积神经网络的肿瘤分割方法，采用多种序列图像，结合 2D 和 3D 上下文，通过级联 2D-3D、子网络处理缺失图像序列等策略提高了效率，在 BRATS 2017 竞赛中良好表现，可用于各种分割任务。