Noise Removed Inconsistency Activation Map for Unsupervised Registration of Brain Tumor MRI Between Pre-operative and Follow-Up Phases

Chongwei Wu, Xiaoyu Zeng, Hao Wang, Xu Zhang, Wei Fang, Qiang Li, Zhiwei Wang

Abstract: Structure inconsistency is the key challenge in registration of brain MRI between pre-operative and follow-up phases, which misguides the objective of image similarity maximization, and thus degrades the performance significantly. The current solutions rely on bidirectional registration to find the mismatched deformation fields as the inconsistent areas, and use them to filter out the unreliable similarity measurements. However, this is sensitive to the accumulated registration errors, and thus yields inaccurate inconsistent areas. In this paper, we provide a more efficient and accurate way, by letting the registration model itself to ‘speak out’ a Noise Removed Inconsistency Activation Map (NR-IAM) as the indicator of structure inconsistencies. We first obtain an IAM by use of the gradient-weighted feature maps but adopting an inverse direction. With this manner only, the resulting inconsistency map often occurs false highlights near some common structures like venous sinus. Therefore, we further introduce a statistical approach to remove the common erroneous activations in IAM to obtain NR-IAM. The experimental results on both public and private datasets demonstrate that by use of our proposed NR-IAM to guide the optimization, the registration performance can be significantly boosted, and is superior over that relying on the bidirectional registration by decreasing mean registration error by 5% and 4% in near tumor and far from tumor regions, respectively. Codes are available at https://github.com/chongweiwu/NR-IAM.