# Fiducial marker recovery and detection from severely truncated data in navigation assisted spine surgery

arXiv: https://arxiv.org/abs/2108.13844 · HTML: https://mlsanity.com/paper/2108.13844/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 56 references that are in the map.
- First posted on arXiv 25 Aug 2021.

## Builds on (most cited first, 8 of 8)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Deep Convolutional Neural Network for Inverse Problems in Imaging](https://mlsanity.com/paper/1611.03679/) (2016)
- [On instabilities of deep learning in image reconstruction - Does AI come at a cost?](https://mlsanity.com/paper/1902.05300/) (2019)
- [Scale-Space Anisotropic Total Variation for Limited Angle Tomography](https://mlsanity.com/paper/1712.06930/) (2017)
- [Field of View Extension in Computed Tomography Using Deep Learning Prior](https://mlsanity.com/paper/1911.01178/) (2019)
- [Limited Angle Tomography for Transmission X-Ray Microscopy Using Deep Learning](https://mlsanity.com/paper/2001.02469/) (2020)
- [CT Field of View Extension Using Combined Channels Extension and Deep Learning Methods](https://mlsanity.com/paper/1908.09529/) (2019)

## Built on by (most cited first, 5 of 5)

- [Artifact Reduction in 3D and 4D Cone-beam Computed Tomography Images with Deep Learning -- A Review](https://mlsanity.com/paper/2403.18565/) (2024)
- [BPF Algorithms for Multiple Source-Translation Computed Tomography Reconstruction](https://mlsanity.com/paper/2305.18878/) (2023)
- [OSNet & MNetO: Two Types of General Reconstruction Architectures for Linear Computed Tomography in Multi-Scenarios](https://mlsanity.com/paper/2309.11858/) (2023)
- [Efficient Image-to-Image Schr"odinger Bridge for CT Field of View Extension](https://mlsanity.com/paper/2508.11211/) (2025)
- [Task-Specific Data Preparation for Deep Learning to Reconstruct Structures of Interest from Severely Truncated CBCT Data](https://mlsanity.com/paper/2409.08800/) (2024)

## Abstract (arXiv)

Fiducial markers are commonly used in navigation assisted minimally invasive spine surgery (MISS) and they help transfer image coordinates into real world coordinates. In practice, these markers might be located outside the field-of-view (FOV), due to the limited detector sizes of C-arm cone-beam computed tomography (CBCT) systems used in intraoperative surgeries. As a consequence, reconstructed markers in CBCT volumes suffer from artifacts and have distorted shapes, which sets an obstacle for navigation. In this work, we propose two fiducial marker detection methods: direct detection from distorted markers (direct method) and detection after marker recovery (recovery method). For direct detection from distorted markers in reconstructed volumes, an efficient automatic marker detection method using two neural networks and a conventional circle detection algorithm is proposed. For marker recovery, a task-specific learning strategy is proposed to recover markers from severely truncated data. Afterwards, a conventional marker detection algorithm is applied for position detection. The two methods are evaluated on simulated data and real data, both achieving a marker registration error smaller than 0.2 mm. Our experiments demonstrate that the direct method is capable of detecting distorted markers accurately and the recovery method with task-specific learning has high robustness and generalizability on various data sets.