# Towards Markerless Intraoperative Tracking of Deformable Spine Tissue

arXiv: https://arxiv.org/abs/2506.23657 · HTML: https://mlsanity.com/paper/2506.23657/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 27 references that are in the map.
- First posted on arXiv 30 Jun 2025.

## Builds on (most cited first, 6 of 6)

- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://mlsanity.com/paper/1706.02413/) (2017)
- [Automatic registration with continuous pose updates for marker-less surgical navigation in spine surgery](https://mlsanity.com/paper/2308.02917/) (2023)
- [Vertebrae localization, segmentation and identification using a graph optimization and an anatomic consistency cycle](https://mlsanity.com/paper/2110.12177/) (2021)
- [Automatic Landmarks Correspondence Detection in Medical Images with an Application to Deformable Image Registration](https://mlsanity.com/paper/2109.02722/) (2021)
- [Occlusion-robust Visual Markerless Bone Tracking for Computer-Assisted Orthopaedic Surgery](https://mlsanity.com/paper/2108.10608/) (2021)

## Built on by (most cited first, 1 of 1)

- [End2Reg: Learning Task-Specific Segmentation for Markerless Registration in Spine Surgery](https://mlsanity.com/paper/2512.13402/) (2025)

## Abstract (arXiv)

Consumer-grade RGB-D imaging for intraoperative orthopedic tissue tracking is a promising method with high translational potential. Unlike bone-mounted tracking devices, markerless tracking can reduce operating time and complexity. However, its use has been limited to cadaveric studies. This paper introduces the first real-world clinical RGB-D dataset for spine surgery and develops SpineAlign, a system for capturing deformation between preoperative and intraoperative spine states. We also present an intraoperative segmentation network trained on this data and introduce CorrespondNet, a multi-task framework for predicting key regions for registration in both intraoperative and preoperative scenes.