FocalErrorNet: Uncertainty-aware focal modulation network for
  inter-modal registration error estimation in ultrasound-guided neurosurgery
Soorena Salari Amirhossein Rasoulian Hassan Rivaz Yiming Xiao
In brain tumor resection, accurate removal of cancerous tissues while
preserving eloquent regions is crucial to the safety and outcomes of the
treatment. However, intra-operative tissue deformation (called brain shift) can
move the surgical target and render the pre-surgical plan invalid.
Intra-operative ultrasound (iUS) has been adopted to provide real-time images
to track brain shift, and inter-modal (i.e., MRI-iUS) registration is often
required to update the pre-surgical plan. Quality control for the registration
results during surgery is important to avoid adverse outcomes, but manual
verification faces great challenges due to difficult 3D visualization and the
low contrast of iUS. Automatic algorithms are urgently needed to address this
issue, but the problem was rarely attempted. Therefore, we propose a novel deep
learning technique based on 3D focal modulation in conjunction with uncertainty
estimation to accurately assess MRI-iUS registration errors for brain tumor
surgery. Developed and validated with the public RESECT clinical database, the
resulting algorithm can achieve an estimation error of 0.59+-0.57 mm.