National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, 518060, China; Faculty of Electrical, Biomedical and Mechatronics Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran.
National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, 518060, China.
Neural Netw. 2020 Dec;132:43-52. doi: 10.1016/j.neunet.2020.08.014. Epub 2020 Aug 18.
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）通过磁场呈现内部器官的详细图像。鉴于 MRI 在重复成像方面具有非侵入性优势，目标区域中的低对比度 MR 图像使得组织分割成为一个具有挑战性的问题。本研究通过图像到图像的转换技术展示了合成高组织对比度（HTC）图像的潜在优势。主要是，我们使用了一种新颖的循环生成对抗网络（Cycle-GAN），它提供了一种注意力机制，以增加组织内的对比度。注意力块和 HTC 图像的训练有利于我们的模型增强组织可见度。我们使用多阶段架构专注于单个组织，作为初步步骤，并在每个阶段过滤掉不相关的上下文，以提高 HTC 图像的分辨率。多阶段架构缩小了源域和目标域之间的差距，并减轻了合成图像的伪影。我们将我们的 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使用 HTC MR 图像。在 BraTS'18 上的三个分割基线的实验中，将合成 HTC 图像加入多模态分割框架后，整个肿瘤（WT）、肿瘤核心（TC）和增强肿瘤（ET）的平均 Dice 相似性得分（DSC）分别提高了 0.8%、0.6%和 0.5%，同时从分割管道中去除了一个真实的 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，与最先进的分割技术相比，脑脊液（CSF）、灰质（GM）和白质（WM）的 DSC 提高了约 1%。合成 HTC 图像的源代码是公开的。
