Marker Detection Using Deep Learning for Universal Navigation Interface

Type: MA thesis

Status: finished

Date: August 20, 2020 - February 22, 2021

Supervisors: Yixing Huang, Andreas Maier, Tino Haderlein

In the contemporary practice of medicine, minimally invasive spine surgery (MISS) is widely performed to avoid
the damage to the muscles surrounding the spine. Compared with traditional open surgeries, patients with MISS
suffer from less pain and can recover faster. For MISS, computer assisted navigation systems play an very important
role. Image guided navigation can deliver more accurate pedicle screw placement compared to conventional surgical
techniques. It also reduces the amount of X-ray exposure to surgeons and patients. In computer assisted navigation
for MISS, registration between preoperative images (typically 3D CT volumes) and intraoperative images (typically
2D fluoroscopic X-ray images) is usually a step of critical importance. To perform such registration, various markers
[1] are used. Such markers need to be identified in the preoperative CT volumes. In practice, due to the limited
detector size, the markers might be located outside the field-of-view of the imaging systems (typically C-arm or Oarm
systems) for large patients. Therefore, the markers are only acquired in projections of a certain view angles. As a
consequence, the reconstructed markers in the 3D CT volumes suffer from artifacts and have distorted shapes, which
cause difficulty for marker detection. In the scope of this master’s thesis, we aim to improve the image quality of CT
reconstructions from such truncated projections using deep learning [2, 3] so that a universal navigation interface is
able to detect markers without any vendor specific information. Alternatively, general marker detection directly in
X-ray projection images before 3D reconstruction using deep learning will also be investigated.

The thesis will include the following points:

 Literature review on deep learning CT truncation correction and deep learning marker detection;

 Simulation of CT data with various marker sizes and shapes;

 Implementation of our U-Net based deep learning method [3] with extension to high resolution reconstruction;

 Performance evaluation of our U-Net based deep learning method on the application of marker reconstruction;

 Investigation of deep learning methods on marker segmentation directly in 2D projections;

 Reconstruction of 3D markers based on segmented marker projections.

[1] S. Virk and S. Qureshi, “Navigation in minimally invasive spine surgery,” Journal of Spine Surgery, vol. 5,
no. Suppl 1, p. S25, 2019.
[2] ´ E. Fourni´e, M. Baer-Beck, and K. Stierstorfer, “CT field of view extension using combined channels extension
and deep learning methods,” in Proceedings of Medical Imaging with Deep Learning, 2019.
[3] Y. Huang, L. Gao, A. Preuhs, and A. Maier, “Field of view extension in computed tomography using deep learning
prior,” in Bildverarbeitung f¨ur die Medizin 2020, pp. 186–191, Springer, 2020.