论文标题
脑肿瘤序列使用非著作的粗到精细网络和双重监督
Brain Tumor Sequence Registration with Non-iterative Coarse-to-fine Networks and Dual Deep Supervision
论文作者
论文摘要
在这项研究中，在脑肿瘤序列注册挑战的背景下（Brats-Reg 2022），我们着重于术前和随访磁共振成像（MRI）扫描（MRI）扫描（MRI）扫描（MRI）。脑肿瘤注册是脑膜图像分析的基本要求，以量化肿瘤变化。由于术前和随访扫描之间的较大变形和缺少对应关系，这是一项具有挑战性的任务。对于此任务，我们采用了最近提出的非著作的粗到1个登记网络（NICE-NET） - 一种基于大型学习的深度学习方法，用于较大变形的图像。为了克服缺失的对应关系，我们通过引入双重深度监督扩展了尼斯网络，在该双重深度监督下，基于图像相似性的深度自我监督损失以及基于手动注释的地标的深度弱的监督损失深深地嵌入了尼斯网络中。在Brats-Reg 2022上，我们的方法在验证集（平均绝对误差：3.387）上取得了竞争性结果，并在最终测试阶段排名第四（得分：0.3544）。
In this study, we focus on brain tumor sequence registration between pre-operative and follow-up Magnetic Resonance Imaging (MRI) scans of brain glioma patients, in the context of Brain Tumor Sequence Registration challenge (BraTS-Reg 2022). Brain tumor registration is a fundamental requirement in brain image analysis for quantifying tumor changes. This is a challenging task due to large deformations and missing correspondences between pre-operative and follow-up scans. For this task, we adopt our recently proposed Non-Iterative Coarse-to-finE registration Networks (NICE-Net) - a deep learning-based method for coarse-to-fine registering images with large deformations. To overcome missing correspondences, we extend the NICE-Net by introducing dual deep supervision, where a deep self-supervised loss based on image similarity and a deep weakly-supervised loss based on manually annotated landmarks are deeply embedded into the NICE-Net. At the BraTS-Reg 2022, our method achieved a competitive result on the validation set (mean absolute error: 3.387) and placed 4th in the final testing phase (Score: 0.3544).
