3D U-Net Improves Automatic Brain Extraction for Isotropic Rat Brain Magnetic Resonance Imaging Data.
3D U-Net Improves Automatic Brain Extraction for Isotropic Rat Brain Magnetic Resonance Imaging Data.
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DOI：
10.3389/fnins.2021.801008
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发表时间：
2021
影响因子：
4.3
通讯作者：
Shih YI
中科院分区：
文献类型：
--
作者：
Hsu LM;Wang S;Walton L;Wang TW;Lee SH;Shih YI
脑提取是脑磁共振成像（MRI）分析流程中的关键预处理步骤。在啮齿动物中，这通常是通过逐片手动编辑大脑掩码来实现的，这是一项耗时的任务，其中工作量随着空间分辨率数据集的增加而增加。我们最近通过基于深度学习的框架U-Net使用2D卷积成功地展示了自动大脑提取。然而，这样的方法不能利用来自体积MRI数据的丰富的3D空间上下文信息。在这项研究中，我们通过将所有2D操作替换为3D操作来改进我们先前提出的U-Net架构，并创建了一个3D U-Net框架。我们使用最近发布的以各向同性空间分辨率获取的CAMRI大鼠大脑数据库训练和验证了我们的模型，包括T2加权快速自旋回波结构MRI和T2* 加权回波平面成像功能MRI。我们的3D U-Net模型的性能与现有的啮齿动物大脑提取工具进行了比较，包括快速自动组织分割，脉冲耦合神经网络，形态学过滤后选择外部区域的形状描述符，以及我们以前提出的2D U-Net模型。3D U-Net在Dice、Jaccard、质心距离、Hausdorff距离和灵敏度方面表现出上级性能。此外，我们还证明了3D U-Net在各种噪声水平下的可靠性，评估了最佳训练样本大小，并公开了所有源代码，希望这种方法将有利于啮齿动物MRI研究界。重要的方法学贡献：我们提出了一个基于深度学习的框架来自动识别MRI中的啮齿动物大脑边界。通过完全3D卷积网络模型3D U-Net，我们提出的方法与当前的自动大脑提取方法相比表现出了更好的性能，如几个定性指标（Dice，Jaccard，PPV，SEN和Hausdorff）所示。我们相信，该工具将避免人为偏见，并简化预处理步骤，在3D高分辨率啮齿动物大脑MRI数据分析。在此开发的软件已免费分发给社区。
Brain extraction is a critical pre-processing step in brain magnetic resonance imaging (MRI) analytical pipelines. In rodents, this is often achieved by manually editing brain masks slice-by-slice, a time-consuming task where workloads increase with higher spatial resolution datasets. We recently demonstrated successful automatic brain extraction via a deep-learning-based framework, U-Net, using 2D convolutions. However, such an approach cannot make use of the rich 3D spatial-context information from volumetric MRI data. In this study, we advanced our previously proposed U-Net architecture by replacing all 2D operations with their 3D counterparts and created a 3D U-Net framework. We trained and validated our model using a recently released CAMRI rat brain database acquired at isotropic spatial resolution, including T2-weighted turbo-spin-echo structural MRI and T2*-weighted echo-planar-imaging functional MRI. The performance of our 3D U-Net model was compared with existing rodent brain extraction tools, including Rapid Automatic Tissue Segmentation, Pulse-Coupled Neural Network, SHape descriptor selected External Regions after Morphologically filtering, and our previously proposed 2D U-Net model. 3D U-Net demonstrated superior performance in Dice, Jaccard, center-of-mass distance, Hausdorff distance, and sensitivity. Additionally, we demonstrated the reliability of 3D U-Net under various noise levels, evaluated the optimal training sample sizes, and disseminated all source codes publicly, with a hope that this approach will benefit rodent MRI research community. Significant Methodological Contribution: We proposed a deep-learning-based framework to automatically identify the rodent brain boundaries in MRI. With a fully 3D convolutional network model, 3D U-Net, our proposed method demonstrated improved performance compared to current automatic brain extraction methods, as shown in several qualitative metrics (Dice, Jaccard, PPV, SEN, and Hausdorff). We trust that this tool will avoid human bias and streamline pre-processing steps during 3D high resolution rodent brain MRI data analysis. The software developed herein has been disseminated freely to the community.
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