Magnetic resonance imaging (MRI) presents a detailed image of the internal organs via a magnetic field. Given MRI’s non-invasive advantage in repeated imaging, the low-contrast MR images in the target area make segmentation of tissue a challenging problem. This study shows the potential advantages of synthetic high tissue contrast (HTC) images through image-to-image translation techniques. Mainly, we use a novel cycle generative adversarial network (Cycle-GAN), which provides a mechanism of attention to increase the contrast within the tissue. The attention block and training on HTC images are beneficial to our model to enhance tissue visibility. We use a multistage architecture to concentrate on a single tissue as a preliminary and filter out the irrelevant context in every stage in order to increase the resolution of HTC images. The multistage architecture reduces the gap between source and target domains and alleviates synthetic images’ artefacts. We apply our HTC image synthesising method to two public datasets. In order to validate the effectiveness of these images we use HTC MR images in both end-to-end and two-stage segmentation structures. The experiments on three segmentation baselines on BraTS’18 demonstrate that joining the synthetic HTC images in the multimodal segmentation framework develops the average Dice similarity scores (DSCs) of 0.8%, 0.6%, and 0.5% respectively on the whole tumour (WT), tumour core (TC), and enhancing tumour (ET) while removing one real MRI channels from the segmentation pipeline. Moreover, segmentation of infant brain tissue in T1w MR slices through our framework improves DSCs approximately 1% in cerebrospinal fluid (CSF), grey matter (GM), and white matter (WM) compared to state-of-the-art segmentation techniques. The source code of synthesising HTC images is publicly available.

磁共振成像（MRI）通过磁场显示内部器官的详细图像。鉴于MRI在重复成像中的非侵入性优势，目标区域中的低对比度MR图像使组织分割成为一个难题。这项研究显示了通过图像间转换技术合成的高组织对比度（HTC）图像的潜在优势。主要地，我们使用一种新型的循环产生对抗网络（Cycle-GAN），该网络提供了一种提高组织内对比度的机制。HTC图像上的注意块和训练对我们的模型有益以增强组织可见性。我们使用多阶段架构将其集中在单个组织上作为初始对象，并在每个阶段过滤掉无关的上下文，以提高HTC图像的分辨率。多级体系结构缩小了源域和目标域之间的距离，并减轻了合成图像的伪像。我们将HTC图像合成方法应用于两个公共数据集。为了验证这些图像的有效性，我们在端到端和两阶段分割结构中都使用了HTC MR图像。在BraTS'18上的三个分割基线上进行的实验表明，在多峰分割框架中加入合成的HTC图像后，整个肿瘤（WT）的平均Dice相似度得分（DSC）分别为0.8％，0.6％和0.5％，肿瘤核心（TC），并增强肿瘤（ET），同时从分割管线中删除一条真实的MRI通道。此外，通过我们的框架对T1w MR切片中的婴儿脑组织进行分割可将脑脊液（CSF）中的DSC改善约1％，灰质（GM）和白质（WM）与最新的分割技术相比。合成HTC图像的源代码是公开可用的。