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Front Matter: Volume 11050

Hiroshi Fujita, Feng Lin, Jong Hyo Kim
2019 International Forum on Medical Imaging in Asia 2019  
image and ultrasonic microscopic images at multiple frequencies 11050 0V Automatic liver segmentation with CT images based on 3D U-net deep learning approach 11050 0W Detection of pulmonary nodules  ...  in whole-body CT images 11050 0Y Automated segmentation framework of lung gross tumor volumes on 3D planning CT images using dense V-Net deep learning 11050 0Z Diagnosis of lymph node metastasis in  ... 
doi:10.1117/12.2530960 fatcat:ee6mypk4vvfgjk22j222eoaydm

Simulation-Driven Training of Vision Transformers Enabling Metal Segmentation in X-Ray Images [article]

Fuxin Fan, Ludwig Ritschl, Marcel Beister, Ramyar Biniazan, Björn Kreher, Tristan M. Gottschalk, Steffen Kappler, Andreas Maier
2022 arXiv   pre-print
Due to the high attenuation of metals, severe artifacts occur in the 3D X-ray acquisitions.  ...  The CNN encoder-based network like U-Net has limited performance on cadaver test data with an average dice score below 0.30, while the metal segmentation transformer with dual decoder (MST-DD) shows high  ...  To reduce the impact of metal artifacts, many algorithms have been developed, such as metal artifact avoidance (MAA) and metal artifact reduction (MAR) method.  ... 
arXiv:2203.09207v1 fatcat:yvwijciycjbzrcup4uzrpktnm4

Metal Artifact Reduction in Spectral X-ray CT Using Spectral Deep Learning

Matteo Busi, Christian Kehl, Jeppe R. Frisvad, Ulrik L. Olsen
2022 Journal of Imaging  
We present a correction method for metal artifact reduction in SCT that is based on spectral deep learning. The correction efficiently reduces streaking artifacts in all the energy channels measured.  ...  Compared with the conventional X-ray CT, this technique provides spectral photon energy resolution in a finite number of energy channels, adding a new dimension to the reconstructed volumes and images.  ...  Spectral CNN Architecture As this work focuses on spectral X-ray CT, the U-Net architecture present in literature required an adaptation that enables the extraction of the additional information in the  ... 
doi:10.3390/jimaging8030077 pmid:35324632 pmcid:PMC8951646 fatcat:jzpwqucww5cc7gnkvxvleummsq

A two-stage approach for beam hardening artifact reduction in low-dose dental CBCT

Taigyntuya Bayaraa, Chang Min Hyun, Tae Jun Jang, Sung Min Lee, Jin Keun Seo
2020 IEEE Access  
In the presence of high-attenuation objects such as metal, the sinogram inconsistency between P and the reconstruction model (based on the assumption VOLUME xxx, 2019 Given sinogram (v = 0 slice) − − u  ...  Beam hardening artifact reduction results using a numerical model.  ... 
doi:10.1109/access.2020.3044981 fatcat:kald5osoprhhdgaruohsfsbmym

Adaptive Convolutional Dictionary Network for CT Metal Artifact Reduction [article]

Hong Wang, Yuexiang Li, Deyu Meng, Yefeng Zheng
2022 arXiv   pre-print
Inspired by the great success of deep neural networks, learning-based methods have gained promising performances for metal artifact reduction (MAR) in computed tomography (CT) images.  ...  Furthermore, our ACDNet can automatically learn the prior for artifact-free CT images via training data and adaptively adjust the representation kernels for each input CT image based on its content.  ...  PCL2021A12, the Key-Area Research and Development Program of Guangdong Province, China (No. 2018B010111001), National Key R&D Program of China (2018YFC2000702), the Scientific and Technical Innovation  ... 
arXiv:2205.07471v2 fatcat:vdr5be2sdvbe7an6ylnccgsc3y

A two-stage approach for beam hardening artifact reduction in low-dose dental CBCT [article]

T. Bayaraa, C. M. Hyun, T. J. Jang, S. M. Lee, J. K. Seo
2020 arXiv   pre-print
The proposed artifact reduction method is designed to improve the quality of maxillofacial imaging, where soft tissue details are not required.  ...  This sinogram correction algorithm significantly reduces beam hardening artifacts caused by high-density materials such as teeth, bones, and metal implants, while tending to amplify special types of noise  ...  Beam hardening artifact reduction results using a numerical model.  ... 
arXiv:2010.03778v1 fatcat:gywqbdxpxjhpxc5v2sg5heydee

Dynamic Pacemaker Artifact Removal (DyPAR) from CT Data using CNNs

Tanja Lossau, Hannes Nickisch, Tobias Wissel, Samer Hakmi, Clemens Spink, Michael M. Morlock, Michael Grass
2019 International Conference on Medical Imaging with Deep Learning  
Furthermore, cardiac motion precludes the application of standard metal artifact reduction methods which assume that the object does not move.  ...  CT image volumes.  ...  Quantitative validation studies are required to assess the transferability of these promising initial results to pacemaker CT artifact reduction in clinical practice.  ... 
dblp:conf/midl/LossauNWHSMG19 fatcat:saou46s2cjc6jm3vngvgfxytty

Deep Learning Based Metal Artifacts Reduction in Post-operative Cochlear Implant CT Imaging [chapter]

Zihao Wang, Clair Vandersteen, Thomas Demarcy, Dan Gnansia, Charles Raffaelli, Nicolas Guevara, Hervé Delingette
2019 Lecture Notes in Computer Science  
Our approach is based on a 3D generative adversarial network (MARGANs) to create an image with a reduction of metal artifacts.  ...  To assess the quality of insertion of Cochlear Implants (CI) after surgery, it is important to analyze the positions of the electrodes with respect to the cochlea based on post-operative CT imaging.  ...  Acknowledgements This work was partially funded by the regional council of Provence Alpes Côte d'Azur, by the French government through the UCA JEDI "Investments in the Future" project managed by the National  ... 
doi:10.1007/978-3-030-32226-7_14 fatcat:poy4oc3lnzhanjjmaioklstywe

Metal Artifact Reduction In Cone-Beam Extremity Images Using Gated Convolutions

Harshit Agrawal, Ari Hietanen, Simo Sarkka
2021 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)  
However, generalization of results for more than one body part is still not investigated.  ...  Our method shows promising results both in projections and reconstructed images. Index Termscone-beam computed tomography, deep learning, metal artifact reduction, orthopedic imaging.  ...  Metal artifact reduction (MAR) has been an active area of research for the past four decades.  ... 
doi:10.1109/isbi48211.2021.9434163 fatcat:yxwp7jjrrfcubbqfehgrm7o7xy

Pose-Aware Instance Segmentation Framework from Cone Beam CT Images for Tooth Segmentation [article]

Minyoung Chung, Minkyung Lee, Jioh Hong, Sanguk Park, Jusang Lee, Jingyu Lee, Jeongjin Lee, Yeong-Gil Shin
2020 arXiv   pre-print
The result shows that our proposed method outperforms other state-of-the-art methods, especially for teeth with metal artifacts.  ...  Metal-intensive image augmentation is also employed for a robust segmentation of metal artifacts.  ...  The original 3D U-net [26] and others failed to segment the teeth with metal artifacts (Figs. 9 and 10).  ... 
arXiv:2002.02143v1 fatcat:4kpz2aoxejcwdnnnaoqxvsqaxq

Encoding Metal Mask Projection for Metal Artifact Reduction in Computed Tomography [article]

Yuanyuan Lyu, Wei-An Lin, Haofu Liao, Jingjing Lu, S. Kevin Zhou
2020 arXiv   pre-print
Metal artifact reduction (MAR) in computed tomography (CT) is a notoriously challenging task because the artifacts are structured and non-local in the image domain.  ...  Extensive experiments on simulated datasets and expert evaluations on clinical images demonstrate that our novel network yields anatomically more precise artifact-reduced images than the state-of-the-art  ...  The artifacts degrade the image quality of CT and its diagnostic value. The challenge of metal artifact reduction (MAR) aggravates when metallic objects are large.  ... 
arXiv:2001.00340v3 fatcat:gz7s4abakbesbm7r6qiywrbzqm

View-Consistent Metal Segmentation in the Projection Domain for Metal Artifact Reduction in CBCT – An Investigation of Potential Improvement [article]

Tristan M. Gottschalk, Andreas Maier, Florian Kordon, Björn W. Kreher
2021 arXiv   pre-print
Due to occurring metal artifacts, the quality of this evaluation heavily depends on the performance of so-called Metal Artifact Reduction methods (MAR).  ...  Therefore, typically a rather simple thresholding-based segmentation method in the reconstructed 3D volume is applied, despite some major disadvantages.  ...  Disclaimer The methods and information presented here are based on research and are not commercially available.  ... 
arXiv:2112.02101v1 fatcat:ikvlxmypirdkjcki5gg7wexuci

A Review of the Methods on Cobb Angle Measurements for Spinal Curvature

Chen Jin, Shengru Wang, Guodong Yang, En Li, Zize Liang
2022 Sensors  
With the development of computer vision and deep learning, it is possible to automatically calculate the Cobb angles by processing X-ray or CT/MR/US images.  ...  In this paper, the research progress of Cobb angle measurement in recent years is reviewed from the perspectives of computer vision and deep learning.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/s22093258 pmid:35590951 pmcid:PMC9101880 fatcat:sa2mkaixofgovh7lt6ih5vyvpe

The application of metal artifact reduction methods on computed tomography scans for radiotherapy applications: A literature review

Sathyathas Puvanasunthararajah, Davide Fontanarosa, Marie-Luise Wille, Saskia M Camps
2021 Journal of Applied Clinical Medical Physics  
Metal artifact reduction (MAR) methods are used to reduce artifacts from metals or metal components in computed tomography (CT).  ...  Conclusion: The application of MAR methods on CT scans can improve treatment planning quality in RT.  ...  published on algorithms which perform metal artifact reduction (MAR) on CT scans.  ... 
doi:10.1002/acm2.13255 pmid:33938608 fatcat:yhuo2hpzifbq5bslms7h7lcdu4

We Know Where We Don't Know: 3D Bayesian CNNs for Credible Geometric Uncertainty [article]

Tyler LaBonte, Carianne Martinez, Scott A. Roberts
2020 arXiv   pre-print
We present experimental results on CT scans of graphite electrodes and laser-welded metals and show that our BCNN outperforms an MCDN in recent uncertainty metrics.  ...  Deep learning has been successfully applied to the segmentation of 3D Computed Tomography (CT) scans.  ...  Closely afterward, [3] proposed 3D U-Net, a direct extension of the U-Net to a 3D domain.  ... 
arXiv:1910.10793v2 fatcat:yftllyfdune2xgjjhfe6lhafxi
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