Two-step registration of rigid and non-rigid MR-iUS for brain shift compensation using transfer learning

Accurate removal of brain tumors is always one of the most important challenges for surgeons, as the continuous change of the brain state after opening the skull and releasing the resulting pressure causes the tumor state to change. By registration of preoperative MR images on intraoperative ultrasound images, the extent of this change is estimated and a new image of the brain is created. The result shows the changes and shifts in the brain, and the surgeon removes the tumor based on this image. Image registration using new deep learning methods has attracted the attention of many researchers due to its high efficiency and accuracy. In this paper, the images of 22 male and female patients with grade 2 glioma tumors were used to evaluate the proposed method. MR images of the patients were taken before surgery, while ultrasound images were taken during surgery and after cranial incision. The deep network used in this paper to compensate for non-rigid changes is voxel morph. All images were fed to the pre-trained network in pairs, and the results are reported for each individual. The average error of all images for the proposed method is 3.56 ± 1.72. This shows the improvement in performance compared to the previous methods since in this work the landmarks were not used in training phase.