School of Life & Environmental Science, Guangxi Colleges and Universities Key Laboratory of Biomedical Sensors and Intelligent Instruments, Guilin University of Electronic Technology, Guilin, China.
State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.
Med Phys. 2023 Sep;50(9):5609-5620. doi: 10.1002/mp.16387. Epub 2023 Mar 31.
Image registration technology has become an important medical image preprocessing step with the wide application of computer-aided diagnosis technology in various medical image analysis tasks.
We propose a multiscale feature fusion registration based on deep learning to achieve the accurate registration and fusion of head magnetic resonance imaging (MRI) and solve the problem that general registration methods cannot handle the complex spatial information and position information of head MRI.
Our proposed multiscale feature fusion registration network consists of three sequentially trained modules. The first is an affine registration module that implements affine transformation; the second is to realize non-rigid transformation, a deformable registration module composed of top-down and bottom-up feature fusion subnetworks in parallel; and the third is a deformable registration module that also realizes non-rigid transformation and is composed of two feature fusion subnetworks in series. The network decomposes the deformation field of large displacement into multiple deformation fields of small displacement by multiscale registration and registration, which reduces the difficulty of registration. Moreover, multiscale information in head MRI is learned in a targeted manner, which improves the registration accuracy, by connecting the two feature fusion subnetworks.
We used 29 3D head MRIs for training and seven volumes for testing and calculated the values of the registration evaluation metrics for the new algorithm to register anterior and posterior lateral pterygoid muscles. The Dice similarity coefficient was 0.745 ± 0.021, the Hausdorff distance was 3.441 ± 0.935 mm, the Average surface distance was 0.738 ± 0.098 mm, and the Standard deviation of the Jacobian matrix was 0.425 ± 0.043. Our new algorithm achieved a higher registration accuracy compared with state-of-the-art registration methods.
Our proposed multiscale feature fusion registration network can realize end-to-end deformable registration of 3D head MRI, which can effectively cope with the characteristics of large deformation displacement and the rich details of head images and provide reliable technical support for the diagnosis and analysis of head diseases.
随着计算机辅助诊断技术在各种医学图像分析任务中的广泛应用，图像配准技术已成为医学图像预处理的重要手段。
提出一种基于深度学习的多尺度特征融合配准方法，实现头部磁共振成像（MRI）的精确配准和融合，解决一般配准方法无法处理头部 MRI 复杂空间信息和位置信息的问题。
所提出的多尺度特征融合配准网络由三个依次训练的模块组成。第一个是实现仿射变换的仿射配准模块；第二个是实现非刚体变换的配准模块，由自上而下和自下而上的并行特征融合子网组成；第三个也是实现非刚体变换的配准模块，由两个特征融合子网串联组成。该网络通过多尺度配准和配准将大位移变形场分解为多个小位移变形场，降低了配准难度。此外，通过连接两个特征融合子网，有针对性地学习头部 MRI 的多尺度信息，提高了配准精度。
我们使用 29 个 3D 头部 MRI 进行训练，7 个容积进行测试，并计算了新算法用于注册前后翼外肌的配准评估指标的值。Dice 相似系数为 0.745±0.021，Hausdorff 距离为 3.441±0.935mm，平均表面距离为 0.738±0.098mm，雅可比矩阵的标准差为 0.425±0.043。与先进的配准方法相比，我们的新算法实现了更高的注册精度。
所提出的多尺度特征融合配准网络可以实现 3D 头部 MRI 的端到端可变形配准，可以有效应对头部图像大变形位移和丰富细节的特点，为头部疾病的诊断和分析提供可靠的技术支持。
