Abstract
Abstract In some applications such as 2D-3D registration, undistorted images are required to achieve optimal results. These types of images can be obtained from a distortion-free C-arm (flat-panel detector) or by undistorting the images given from a conventional C-arm (analogue image intensifier.) Undistorting images require a plate with fiducials connected to the C-arm detector. Detecting fiducials is affected by differences in the image contrast due to elements in the background. Therefore, the results vary from image to image and could require manual tuning of parameters. We propose a deep-learning approach for detecting undistortion-platefiducials in X-ray images to overcome the drawbacks previously stated. With an undistortion plate, we took 1120 XRays using a C-arm in different poses. Every X-ray is afterward cut into 60 sub-images. We used these sub-images for training a convolutional neural network (CNN). Comparing the CNN and a traditional image processing method based on the Hough Circle algorithm, we found that the detected fiducials using the traditional method give a similar fiducial positioning error. Nevertheless, the fiducial detection rate goes from 89.7% using the traditional method to 100% with the developed CNN. The results show that the detection rate and precision of our deep-learning approach guarantee the undistortion of conventional C-Arm images.
Highlights
In conventional C-arms, i.e., C-arms equipped with analog image intensifiers, distortions are a mixture of barrel, S-shape, and spiral distortions, and they are caused due to gravitational effects and the C-arm pose
With the idea of having a parameterless and robust detector, we propose to use a deeplearning approach for detecting undistortion-plate-fiducials on X-ray images
We aim to incorporate in our workflow a standalone procedure, which makes an inference over an image with fiducials and later undistort an X-ray image
Summary
In conventional C-arms, i.e., C-arms equipped with analog image intensifiers, distortions are a mixture of barrel, S-shape, and spiral distortions, and they are caused due to gravitational effects and the C-arm pose. That makes it difficult to create a general model for the C-arm distortion because it changes with time and position. Undistorted images are required in some applications, which need patient anatomy to be represented as precise as possible. In the case of 2D-3D registration, for example, it gives optimal results only when the used images are undistorted. Most of the commercial computer-assisted surgery systems use distortionfree C-arms, i.e., C-arms with flat-panel detector, for the registration process. We want to increase the reach of these systems by offering the possibility to incorporate conventional C-arms
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