Reinforced Redetection of Landmark in Pre- and Post-operative Brain Scan Using Anatomical Guidance for Image Alignment

Re-identifying locations of interest in pre- and post-operative images is a hard identification problem, as the anatomical landscape changes dramatically due to tumor resection and tissue displacement. Classical image registration techniques oftentimes fail in vicinity of the tumor, where the enclosing structures are massively altered from one scan to another. Still, locations nearby the tumor or the resection cavity are the most relevant for evaluating tumor progression patterns and for comparing pre- and post-operative radiomic signatures. We address this issue by exploring a Reinforcement Learning (RL) approach. An artificial agent is self-taught to find the optimal path towards a target driven by a feedback signal from the environment. Incorporating anatomical guidance, we restrict the agent’s search space to surgery-unaffected structures only. By defining landmarks for each patient individually, we aim to obtain a patient-specific representation of its differential radiomic features across different time points for enhancing image alignment. Estimated landmarks reach a remarkable mean distance error around 3 mm. In addition, they show a high agreement with expert annotations on a challenging dataset of MR scans from the brain before and after tumor resection.