Batenburg KJ - Search Results

1

A review on the application of deep learning for CT reconstruction, bone segmentation and surgical planning in oral and maxillofacial surgery.

Minnema J, Ernst A, van Eijnatten M, Pauwels R, Forouzanfar T, Batenburg KJ, Wolff J. Minnema J, et al. Among authors: batenburg kj. Dentomaxillofac Radiol. 2022 Sep 1;51(7):20210437. doi: 10.1259/dmfr.20210437. Epub 2022 May 23. Dentomaxillofac Radiol. 2022. PMID: 35532946 Free PMC article. Review.

2

Segmentation of dental cone-beam CT scans affected by metal artifacts using a mixed-scale dense convolutional neural network.

Minnema J, van Eijnatten M, Hendriksen AA, Liberton N, Pelt DM, Batenburg KJ, Forouzanfar T, Wolff J. Minnema J, et al. Among authors: batenburg kj. Med Phys. 2019 Nov;46(11):5027-5035. doi: 10.1002/mp.13793. Epub 2019 Sep 13. Med Phys. 2019. PMID: 31463937 Free PMC article.

3

A cone-beam X-ray computed tomography data collection designed for machine learning.

Der Sarkissian H, Lucka F, van Eijnatten M, Colacicco G, Coban SB, Batenburg KJ. Der Sarkissian H, et al. Among authors: batenburg kj. Sci Data. 2019 Oct 22;6(1):215. doi: 10.1038/s41597-019-0235-y. Sci Data. 2019. PMID: 31641152 Free PMC article.

4

Multiclass CBCT Image Segmentation for Orthodontics with Deep Learning.

Wang H, Minnema J, Batenburg KJ, Forouzanfar T, Hu FJ, Wu G. Wang H, et al. Among authors: batenburg kj. J Dent Res. 2021 Aug;100(9):943-949. doi: 10.1177/00220345211005338. Epub 2021 Mar 30. J Dent Res. 2021. PMID: 33783247 Free PMC article.

5

Comparison of convolutional neural network training strategies for cone-beam CT image segmentation.

Minnema J, Wolff J, Koivisto J, Lucka F, Batenburg KJ, Forouzanfar T, van Eijnatten M. Minnema J, et al. Among authors: batenburg kj. Comput Methods Programs Biomed. 2021 Aug;207:106192. doi: 10.1016/j.cmpb.2021.106192. Epub 2021 May 20. Comput Methods Programs Biomed. 2021. PMID: 34062493 Free article.

6

Efficient high cone-angle artifact reduction in circular cone-beam CT using deep learning with geometry-aware dimension reduction.

Minnema J, van Eijnatten M, der Sarkissian H, Doyle S, Koivisto J, Wolff J, Forouzanfar T, Lucka F, Batenburg KJ. Minnema J, et al. Among authors: batenburg kj. Phys Med Biol. 2021 Jul 1;66(13). doi: 10.1088/1361-6560/ac09a1. Phys Med Biol. 2021. PMID: 34107467

7

3D deformable registration of longitudinal abdominopelvic CT images using unsupervised deep learning.

van Eijnatten M, Rundo L, Batenburg KJ, Lucka F, Beddowes E, Caldas C, Gallagher FA, Sala E, Schönlieb CB, Woitek R. van Eijnatten M, et al. Among authors: batenburg kj. Comput Methods Programs Biomed. 2021 Sep;208:106261. doi: 10.1016/j.cmpb.2021.106261. Epub 2021 Jul 8. Comput Methods Programs Biomed. 2021. PMID: 34289437 Free article.

8

Quantitative Comparison of Deep Learning-Based Image Reconstruction Methods for Low-Dose and Sparse-Angle CT Applications.

Leuschner J, Schmidt M, Ganguly PS, Andriiashen V, Coban SB, Denker A, Bauer D, Hadjifaradji A, Batenburg KJ, Maass P, van Eijnatten M. Leuschner J, et al. Among authors: batenburg kj. J Imaging. 2021 Mar 2;7(3):44. doi: 10.3390/jimaging7030044. J Imaging. 2021. PMID: 34460700 Free PMC article.

9

DART: a practical reconstruction algorithm for discrete tomography.

Batenburg KJ, Sijbers J. Batenburg KJ, et al. IEEE Trans Image Process. 2011 Sep;20(9):2542-53. doi: 10.1109/TIP.2011.2131661. Epub 2011 Mar 24. IEEE Trans Image Process. 2011. PMID: 21435983

10

Adaptive zooming in X-ray computed tomography.

Dabravolski A, Batenburg KJ, Sijbers J. Dabravolski A, et al. Among authors: batenburg kj. J Xray Sci Technol. 2014;22(1):77-89. doi: 10.3233/XST-130410. J Xray Sci Technol. 2014. PMID: 24463387

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