End2Reg: Learning Task-Specific Segmentation for Markerless Registration in Spine Surgery

TL;DR

End2Reg introduces an end-to-end deep learning framework that jointly optimizes segmentation and registration for markerless spine surgery navigation, achieving high accuracy without manual labels.

Contribution

It is the first to eliminate the need for segmentation labels by jointly optimizing segmentation and registration in a deep learning model for spine surgery.

Findings

Reduced median Target Registration Error by 32%

Lowered mean Root Mean Square Error by 61%

Maintained robustness under partial occlusions

Abstract

Intraoperative navigation in spine surgery demands millimeter-level accuracy. Currently, this is achieved through radiation-intensive intraoperative imaging and bone-anchored markers that are invasive and disrupt surgical workflow. Markerless RGB-D registration methods offer a promising alternative. However, existing approaches rely on weak segmentation labels to isolate relevant anatomical structures, potentially propagating errors through the registration process. We present End2Reg, an end-to-end deep learning framework that jointly optimizes segmentation and registration, eliminating the need for segmentation labels and manual steps. The network learns task-specific segmentation masks optimized for registration, guided solely by the registration objective without explicit segmentation supervision. End2Reg achieves state-of-the-art performance on ex- and in-vivo benchmarks, reducing median Target Registration Error by 32% and mean Root Mean Square Error by 61%, while maintaining robust performance under partial occlusions. Ablation results confirm that end-to-end optimization significantly improves registration accuracy. Overall, End2Reg advances towards fully automatic, markerless intraoperative navigation. Code and interactive visualizations are available at: https://lorenzopettinari.github.io/end-2-reg/.