Registration of partial-view 3D US volumes with MRI data is influenced by initialization. The standard of practice is using extrinsic or intrinsic landmarks, which can be very tedious to obtain. To overcome the limitations of registration initialization, we present a novel approach that is based on Euclidean distance maps derived from easily obtainable coarse segmentations. We evaluate our approach on a publicly available brain tumor dataset (RESECT) and show that it is robust regarding minimal to no overlap of target area and varying initial position. We demonstrate that our method provides initializations that greatly increase the capture range of state-of-the-art nonlinear registration algorithms.     «

Registration of partial-view 3D US volumes with MRI data is influenced by initialization. The standard of practice is using extrinsic or intrinsic landmarks, which can be very tedious to obtain. To overcome the limitations of registration initialization, we present a novel approach that is based on Euclidean distance maps derived from easily obtainable coarse segmentations. We evaluate our approach on a publicly available brain tumor dataset (RESECT) and show that it is robust regarding minimal...     »