X-ray-transform Invariant Anatomical Landmark Detection for Pelvic Trauma Surgery

TL;DR

This paper introduces a novel method for automatically detecting anatomical landmarks in pelvic X-ray images that remains accurate regardless of viewing angle, aiding surgical decision-making and image registration.

Contribution

The authors develop a view-invariant landmark detection method using synthetic training data and a convolutional network, addressing a previously unsolved challenge in X-ray image analysis.

Findings

Achieves a mean prediction error of 5.6 mm on synthetic data.

Successfully applies to clinical pelvic X-ray images.

Enables X-ray pose estimation for improved surgical guidance.

Abstract

X-ray image guidance enables percutaneous alternatives to complex procedures. Unfortunately, the indirect view onto the anatomy in addition to projective simplification substantially increase the task-load for the surgeon. Additional 3D information such as knowledge of anatomical landmarks can benefit surgical decision making in complicated scenarios. Automatic detection of these landmarks in transmission imaging is challenging since image-domain features characteristic to a certain landmark change substantially depending on the viewing direction. Consequently and to the best of our knowledge, the above problem has not yet been addressed. In this work, we present a method to automatically detect anatomical landmarks in X-ray images independent of the viewing direction. To this end, a sequential prediction framework based on convolutional layers is trained on synthetically generated data of the pelvic anatomy to predict 23 landmarks in single X-ray images. View independence is contingent on training conditions and, here, is achieved on a spherical segment covering (120 x 90) degrees in LAO/RAO and CRAN/CAUD, respectively, centered around AP. On synthetic data, the proposed approach achieves a mean prediction error of 5.6 +- 4.5 mm. We demonstrate that the proposed network is immediately applicable to clinically acquired data of the pelvis. In particular, we show that our intra-operative landmark detection together with pre-operative CT enables X-ray pose estimation which, ultimately, benefits initialization of image-based 2D/3D registration.