A Deep-Learning Approach to Detect Fiducials in Planar X-Ray Images for Undistortion of Conventional C-Arm Images

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.) Undistorti...

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Autores principales:	Alvarez-Gomez Julio, Arne Spieß, Hubert Roth, Jürgen Wahrburg
Formato:	article
Lenguaje:	EN
Publicado:	De Gruyter 2020
Materias:	deep-learning fiducial detection x-ray image undistortion convolutional neural network c-arm Medicine R
Acceso en línea:	https://doaj.org/article/4bb757a8f10f43879e031055bfa4a21b
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id	oai:doaj.org-article:4bb757a8f10f43879e031055bfa4a21b
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spelling	oai:doaj.org-article:4bb757a8f10f43879e031055bfa4a21b2021-12-05T14:10:42ZA Deep-Learning Approach to Detect Fiducials in Planar X-Ray Images for Undistortion of Conventional C-Arm Images2364-550410.1515/cdbme-2020-3011https://doaj.org/article/4bb757a8f10f43879e031055bfa4a21b2020-09-01T00:00:00Zhttps://doi.org/10.1515/cdbme-2020-3011https://doaj.org/toc/2364-5504In 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.Alvarez-Gomez JulioArne SpießHubert RothJürgen WahrburgDe Gruyterarticledeep-learningfiducial detectionx-rayimage undistortionconvolutional neural networkc-armMedicineRENCurrent Directions in Biomedical Engineering, Vol 6, Iss 3, Pp 40-43 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	deep-learning fiducial detection x-ray image undistortion convolutional neural network c-arm Medicine R
spellingShingle	deep-learning fiducial detection x-ray image undistortion convolutional neural network c-arm Medicine R Alvarez-Gomez Julio Arne Spieß Hubert Roth Jürgen Wahrburg A Deep-Learning Approach to Detect Fiducials in Planar X-Ray Images for Undistortion of Conventional C-Arm Images
description	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.
format	article
author	Alvarez-Gomez Julio Arne Spieß Hubert Roth Jürgen Wahrburg
author_facet	Alvarez-Gomez Julio Arne Spieß Hubert Roth Jürgen Wahrburg
author_sort	Alvarez-Gomez Julio
title	A Deep-Learning Approach to Detect Fiducials in Planar X-Ray Images for Undistortion of Conventional C-Arm Images
title_short	A Deep-Learning Approach to Detect Fiducials in Planar X-Ray Images for Undistortion of Conventional C-Arm Images
title_full	A Deep-Learning Approach to Detect Fiducials in Planar X-Ray Images for Undistortion of Conventional C-Arm Images
title_fullStr	A Deep-Learning Approach to Detect Fiducials in Planar X-Ray Images for Undistortion of Conventional C-Arm Images
title_full_unstemmed	A Deep-Learning Approach to Detect Fiducials in Planar X-Ray Images for Undistortion of Conventional C-Arm Images
title_sort	deep-learning approach to detect fiducials in planar x-ray images for undistortion of conventional c-arm images
publisher	De Gruyter
publishDate	2020
url	https://doaj.org/article/4bb757a8f10f43879e031055bfa4a21b
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A Deep-Learning Approach to Detect Fiducials in Planar X-Ray Images for Undistortion of Conventional C-Arm Images

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