FocalErrorNet: Uncertainty-aware focal modulation network for inter-modal registration error estimation in ultrasound-guided neurosurgery

TL;DR

This paper introduces FocalErrorNet, a deep learning model that uses 3D focal modulation and uncertainty estimation to accurately assess registration errors between MRI and intra-operative ultrasound in brain tumor surgery.

Contribution

The paper presents a novel 3D focal modulation network with uncertainty estimation for automatic registration error assessment in intra-operative brain imaging.

Findings

Achieves an estimation error of 0.59+-0.57 mm on the RESECT database.

Provides a real-time, automatic quality control tool for MRI-iUS registration.

Enhances safety and accuracy in brain tumor resection surgeries.

Abstract

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.