# Learning Expected Appearances for Intraoperative Registration during Neurosurgery

arXiv: https://arxiv.org/abs/2310.01735 · HTML: https://mlsanity.com/paper/2310.01735/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 28 references that are in the map.
- First posted on arXiv 3 Oct 2023.

## Builds on (most cited first, 7 of 7)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Texture Networks: Feed-forward Synthesis of Textures and Stylized Images](https://mlsanity.com/paper/1603.03417/) (2016)
- [Surgical Data Science -- from Concepts toward Clinical Translation](https://mlsanity.com/paper/2011.02284/) (2020)
- [GANs for Medical Image Synthesis: An Empirical Study](https://mlsanity.com/paper/2105.05318/) (2021)
- [Can segmentation models be trained with fully synthetically generated data?](https://mlsanity.com/paper/2209.08256/) (2022)
- [LiftReg: Limited Angle 2D/3D Deformable Registration](https://mlsanity.com/paper/2203.05565/) (2022)
- [CNN-based real-time 2D-3D deformable registration from a single X-ray projection](https://mlsanity.com/paper/2212.07692/) (2022)

## Built on by (most cited first, 3 of 3)

- [Intraoperative Registration by Cross-Modal Inverse Neural Rendering](https://mlsanity.com/paper/2409.11983/) (2024)
- [Monocular Marker-free Patient-to-Image Intraoperative Registration for Cochlear Implant Surgery](https://mlsanity.com/paper/2505.18381/) (2025)
- [Data-driven registration and modeling of brain deformation for image-guided neurosurgery](https://mlsanity.com/paper/2602.10155/) (2026)

## Abstract (arXiv)

We present a novel method for intraoperative patient-to-image registration by learning Expected Appearances. Our method uses preoperative imaging to synthesize patient-specific expected views through a surgical microscope for a predicted range of transformations. Our method estimates the camera pose by minimizing the dissimilarity between the intraoperative 2D view through the optical microscope and the synthesized expected texture. In contrast to conventional methods, our approach transfers the processing tasks to the preoperative stage, reducing thereby the impact of low-resolution, distorted, and noisy intraoperative images, that often degrade the registration accuracy. We applied our method in the context of neuronavigation during brain surgery. We evaluated our approach on synthetic data and on retrospective data from 6 clinical cases. Our method outperformed state-of-the-art methods and achieved accuracies that met current clinical standards.