Segmentation of MRI head anatomy using deep volumetric networks and multiple spatial priors.
Segmentation of MRI head anatomy using deep volumetric networks and multiple spatial priors.
复制标题
使用深度体积网络和多个空间先验对 MRI 头部解剖结构进行分割。
DOI：
10.1117/1.jmi.8.3.034001
复制
发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Parra,LucasC
中科院分区：
文献类型：
--
作者：
Hirsch,Lukas;Huang,Yu;Parra,LucasC
目的：磁共振图像中头部解剖结构的传统自动分割根据图像强度和先验组织概率图（TPM）区分不同的大脑和非大脑组织。这对于正常的头部解剖结构很有效，但在出现意外病变时会失败。深度卷积神经网络 (CNN) 利用空间模式，可以学习分割病变，但通常会忽略先验概率。 方法：我们将三个先验信息源添加到三维 (3D) 卷积网络中，即具有 TPM 的空间先验、具有条件随机场的形态先验以及在较低分辨率下具有更宽视野的空间上下文。我们在 43 名中风患者和 4 名健康个体的 3D 图像上训练和测试这些网络，这些图像已被手动分割。结果：我们展示了每个先验信息源的好处，并且我们表明，我们称之为 Multiprior 网络的新架构提高了现有分割软件（例如 SPM、FSL 和 DeepMedic）针对异常解剖结构的性能。比较了不同先验的相关性，发现 TPM 最为有益。添加 TPM 的好处是通用的，因为它可以提高已建立的分段网络（例如 DeepMedic 和 UNet）的性能。我们还对另外 47 名意识障碍患者提供了该方法的样本外验证和临床应用。我们免费提供代码和经过训练的网络。结论：生物医学图像遵循成像协议，可以作为深度 CNN 的先验信息来提高性能。网络分割与 3D 中执行的人类手动校正相匹配，并且在性能上与 2D 中针对异常大脑解剖结构从头开始获得的人类分割相当。
Purpose:Conventional automated segmentation of the head anatomy in magnetic resonance images distinguishes different brain and nonbrain tissues based on image intensities and prior tissue probability maps (TPMs). This works well for normal head anatomies but fails in the presence of unexpected lesions. Deep convolutional neural networks (CNNs) leverage instead spatial patterns and can learn to segment lesions but often ignore prior probabilities.Approach:We add three sources of prior information to a three-dimensional (3D) convolutional network, namely, spatial priors with a TPM, morphological priors with conditional random fields, and spatial context with a wider field-of-view at lower resolution. We train and test these networks on 3D images of 43 stroke patients and 4 healthy individuals which have been manually segmented.Results:We demonstrate the benefits of each source of prior information, and we show that the new architecture, which we call Multiprior network, improves the performance of existing segmentation software, such as SPM, FSL, and DeepMedic for abnormal anatomies. The relevance of the different priors was compared, and the TPM was found to be most beneficial. The benefit of adding a TPM is generic in that it can boost the performance of established segmentation networks such as the DeepMedic and a UNet. We also provide an out-of-sample validation and clinical application of the approach on an additional 47 patients with disorders of consciousness. We make the code and trained networks freely available.Conclusions:Biomedical images follow imaging protocols that can be leveraged as prior information into deep CNNs to improve performance. The network segmentations match human manual corrections performed in 3D and are comparable in performance to human segmentations obtained from scratch in 2D for abnormal brain anatomies.