Carol C. Hasler

Lorenzo Pettinari, Sidaty El Hadramy, Michael Wehrli et al.

Purpose: Intraoperative navigation in spine surgery demands millimeter-level accuracy. Current systems based on intraoperative radiographic imaging and bone-anchored markers are invasive, radiation-intensive and workflow disruptive. Recent markerless RGB-D registration methods offer a promising alternative, but existing approaches rely on weak segmentation labels to isolate relevant anatomical structures, which can propagate errors throughout registration. Methods: We present End2Reg an end-to-end deep learning framework that jointly optimizes segmentation and registration, eliminating the need for weak segmentation labels and manual steps. The network learns segmentation masks specifically optimized for registration, guided solely by the registration objective without direct segmentation supervision. Results: The proposed framework achieves state-of-the-art performance on ex- and in-vivo benchmarks, reducing median Target Registration Error by 32% to 1.83mm and mean Root Mean Square Error by 45% to 3.95mm, respectively. An ablation study confirms that end-to-end optimization significantly improves registration accuracy. Conclusion: The presented end-to-end RGB-D registration pipeline removes dependency on weak labels and manual steps, advancing towards fully automatic, markerless intraoperative navigation. Code and interactive visualizations are available at: https://lorenzopettinari.github.io/end-2-reg/.

Michael Wehrli, Alicia Durrer, Paul Friedrich et al.

To treat Trochlear Dysplasia (TD), current approaches rely mainly on low-resolution clinical Magnetic Resonance (MR) scans and surgical intuition. The surgeries are planned based on surgeons experience, have limited adoption of minimally invasive techniques, and lead to inconsistent outcomes. We propose a pipeline that generates super-resolved, patient-specific 3D pseudo-healthy target morphologies from conventional clinical MR scans. First, we compute an isotropic super-resolved MR volume using an Implicit Neural Representation (INR). Next, we segment femur, tibia, patella, and fibula with a multi-label custom-trained network. Finally, we train a Wavelet Diffusion Model (WDM) to generate pseudo-healthy target morphologies of the trochlear region. In contrast to prior work producing pseudo-healthy low-resolution 3D MR images, our approach enables the generation of sub-millimeter resolved 3D shapes compatible for pre- and intraoperative use. These can serve as preoperative blueprints for reshaping the femoral groove while preserving the native patella articulation. Furthermore, and in contrast to other work, we do not require a CT for our pipeline - reducing the amount of radiation. We evaluated our approach on 25 TD patients and could show that our target morphologies significantly improve the sulcus angle (SA) and trochlear groove depth (TGD). The code and interactive visualization are available at https://wehrlimi.github.io/sr-3d-planning/.