Deformable image registration is a key task in medical image analysis. The Brain Tumor Sequence Registration challenge (BraTS-Reg) aims at establishing correspondences between pre-operative and follow-up scans of the same patient diagnosed with an adult brain diffuse high-grade glioma and intends to address the challenging task of registering longitudinal data with major tissue appearance changes. In this work, we proposed a two-stage cascaded network based on the Inception and TransMorph models. The dataset for each patient was comprised of a native pre-contrast (T1), a contrast-enhanced T1-weighted (T1-CE), a T2-weighted (T2), and a Fluid Attenuated Inversion Recovery (FLAIR). The Inception model was used to fuse the 4 image modalities together and extract the most relevant information. Then, a variant of the TransMorph architecture was adapted to generate the displacement fields. The Loss function was composed of a standard image similarity measure, a diffusion regularizer, and an edge-map similarity measure added to overcome intensity dependence and reinforce correct boundary deformation. We observed that the addition of the Inception module substantially increased the performance of the network. Additionally, performing an initial affine registration before training the model showed improved accuracy in the landmark error measurements between pre and post-operative MRIs. We observed that our best model composed of the Inception and TransMorph architectures while using an initially affine registered dataset had the best performance with a median absolute error of 2.91 (initial error = 7.8). We achieved 6th place at the time of model submission in the final testing phase of the BraTS-Reg challenge.
翻译:大脑肿瘤序列登记挑战(BraTS-Reg)旨在对被诊断患有成人大脑传播高等级微粒瘤的同一病人进行操作前扫描和后续扫描(BraTS-Reg),目的是解决在组织外观变化中登记纵向数据这一具有挑战性的任务。在这项工作中,我们提出了基于感知和 TransMorph 模型的两阶段级级网络。每个病人的数据集都包含一个本地的直径前(T1)、对比增强的T1加权T1(T1-CE)、T2加权(T2)和Fluid加速反转恢复(FLAIR)之间的对应,并打算解决将4个图像模式结合在一起并提取最相关的信息这一具有挑战性的任务。随后,我们提出了基于感知和 TransMorph 结构的变异功能,以生成迁移字段。损失功能包括一个标准的图像中位值中位度测量、一个扩散调节器,以及一个边缘相近度测量度测量模型,以克服深度依赖性关系和精度后度后度恢复性恢复功能。我们观测到的模型,我们观测到一个已观测到一个已观测到的升级的模型。