Treffer: Selected annotated instance segmentation sub-volumes from a large scale CT data-set of a historic aircraft.
Title:
Selected annotated instance segmentation sub-volumes from a large scale CT data-set of a historic aircraft.
Authors:
Gruber R; Fraunhofer IIS, Fraunhofer Institute for Integrated Circuits IIS, Division Development Center X-Ray Technology, Fürth, Germany. roland.gruber@iis.fraunhofer.de.; Friedrich-Alexander-Universität Erlangen-Nürnberg, Chair for Visual Computing, Erlangen, Germany. roland.gruber@iis.fraunhofer.de., Reims N; Fraunhofer IIS, Fraunhofer Institute for Integrated Circuits IIS, Division Development Center X-Ray Technology, Fürth, Germany., Hempfer A; Deutsches Museum, München, Germany., Gerth S; Fraunhofer IIS, Fraunhofer Institute for Integrated Circuits IIS, Division Development Center X-Ray Technology, Fürth, Germany., Böhnel M; Fraunhofer IIS, Fraunhofer Institute for Integrated Circuits IIS, Division Development Center X-Ray Technology, Fürth, Germany., Fuchs T; Fraunhofer IIS, Fraunhofer Institute for Integrated Circuits IIS, Division Development Center X-Ray Technology, Fürth, Germany., Salamon M; Fraunhofer IIS, Fraunhofer Institute for Integrated Circuits IIS, Division Development Center X-Ray Technology, Fürth, Germany., Wittenberg T; Fraunhofer IIS, Fraunhofer Institute for Integrated Circuits IIS, Division Development Center X-Ray Technology, Fürth, Germany.; Friedrich-Alexander-Universität Erlangen-Nürnberg, Chair for Visual Computing, Erlangen, Germany.
Source:
Scientific data [Sci Data] 2024 Jun 24; Vol. 11 (1), pp. 680. Date of Electronic Publication: 2024 Jun 24.
Publication Type:
Journal Article; Dataset
Language:
English
Journal Info:
Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101640192 Publication Model: Electronic Cited Medium: Internet ISSN: 2052-4463 (Electronic) Linking ISSN: 20524463 NLM ISO Abbreviation: Sci Data Subsets: PubMed not MEDLINE; MEDLINE
Imprint Name(s):
Original Publication: London : Nature Publishing Group, 2014-
References:
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Frisch, J., Blaensdorf, C., Hempfer, A., Pamplona-Bartsch, M. & Grosse, C. U. Active thermography to look beneath the surface of a historic german aircraft. Research and Review Journal of Nondestructive Testing 1, https://doi.org/10.58286/28137 (2023).
Hadwiger, M. et al. Interactive volume exploration for feature detection and quantification in industrial ct data. IEEE Transactions on Visualization and Computer Graphics 14, 1507–1514, https://doi.org/10.1109/TVCG.2008.147 (2008). (PMID: 10.1109/TVCG.2008.14718989003)
Gruber, R. et al.Exploring Flood Filling Networks for Instance Segmentation of XXL-Volumetric and Bulk Material CT Data. Journal of Nondestructive Evaluation 40, https://doi.org/10.1007/s10921-020-00734-w (2021).
Du, W., Shen, H. & Fu, J. Automatic defect segmentation in x-ray images based on deep learning. IEEE Transactions on Industrial Electronics 68, 12912–12920, https://doi.org/10.1109/TIE.2020.3047060 (2021). (PMID: 10.1109/TIE.2020.3047060)
Hafiz, A. M. & Bhat, G. M. A survey on instance segmentation: state of the art. International Journal of Multimedia Information Retrieval 9, 171–189, https://doi.org/10.1007/s13735-020-00195-x (2020). (PMID: 10.1007/s13735-020-00195-x)
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Kikinis, R., Pieper, S. D. & Vosburgh, K. G. 3d slicer: A platform for subject-specific image analysis, visualization, and clinical support. In Jolesz, F. A. (ed.) Intraoperative Imaging and Image-Guided Therapy, 277–289, https://doi.org/10.1007/978-1-4614-7657-3_19 (Springer New York, 2014).
Dach, C., Held, C., Palmisano, R., Wittenberg, T. & Friedl, S. Evaluation of input modalities for the interactive image segmentation of fluorescent micrographs. In Biomed Tech, 56, S1 (2011).
Gonzalez, R. C. & Woods, R. E. Digital Image Processing (3rd Edition) (Prentice-Hall, Inc., USA, 2006).
Tanno, R., Saeedi, A., Sankaranarayanan, S., Alexander, D. C. & Silberman, N. Learning from noisy labels by regularized estimation of annotator confusion. In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 11236–11245, https://doi.org/10.1109/CVPR.2019.01150 (2019).
Vădineanu, C., Pelt, D. M., Dzyubachyk, O. & Batenburg, K. J. An analysis of the impact of annotation errors on the accuracy of deep learning for cell segmentation. In Konukoglu, E. et al. (eds.) Proceedings of The 5th International Conference on Medical Imaging with Deep Learning, vol. 172 of Proceedings of Machine Learning Research, 1251–1267 (PMLR, 2022).
Frisch, J., Blaensdorf, C., Hempfer, A., Pamplona-Bartsch, M. & Grosse, C. U. Active thermography to look beneath the surface of a historic german aircraft. Research and Review Journal of Nondestructive Testing 1, https://doi.org/10.58286/28137 (2023).
Hadwiger, M. et al. Interactive volume exploration for feature detection and quantification in industrial ct data. IEEE Transactions on Visualization and Computer Graphics 14, 1507–1514, https://doi.org/10.1109/TVCG.2008.147 (2008). (PMID: 10.1109/TVCG.2008.14718989003)
Gruber, R. et al.Exploring Flood Filling Networks for Instance Segmentation of XXL-Volumetric and Bulk Material CT Data. Journal of Nondestructive Evaluation 40, https://doi.org/10.1007/s10921-020-00734-w (2021).
Du, W., Shen, H. & Fu, J. Automatic defect segmentation in x-ray images based on deep learning. IEEE Transactions on Industrial Electronics 68, 12912–12920, https://doi.org/10.1109/TIE.2020.3047060 (2021). (PMID: 10.1109/TIE.2020.3047060)
Hafiz, A. M. & Bhat, G. M. A survey on instance segmentation: state of the art. International Journal of Multimedia Information Retrieval 9, 171–189, https://doi.org/10.1007/s13735-020-00195-x (2020). (PMID: 10.1007/s13735-020-00195-x)
Sun, C., Shrivastava, A., Singh, S. & Gupta, A. Revisiting unreasonable effectiveness of data in deep learning era. In 2017 IEEE International Conference on Computer Vision (ICCV), 843–852, https://doi.org/10.1109/ICCV.2017.97 (2017).
Deng, J. et al. Imagenet: A large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition, 248–255 (organizationIeee, 2009).
Lin, T.-Y. et al. Microsoft COCO: Common objects in context. In Computer Vision – ECCV 2014, 740–755, https://doi.org/10.1007/978-3-319-10602-1_48 (Springer International Publishing, 2014).
Gruber, R. et al. Fraunhofer EZRT XXL-CT Instance Segmentation Me163. Zenodo https://doi.org/10.5281/zenodo.10651746 (2024).
Brown, E. M. Wings of the Luftwaffe–Flying German aircraft of the Second World War (MacDonald and Jane´s, London, 1977).
Butlar, P. War Prizes (Midland Counties Publications, Leicester, 1994).
Salamon, M. et al. XXL-CT capabilities for the inspection of modern electric vehicles. In 19th World Conference on Non-Destructive Testing (2016).
Feldkamp, L. A., Davis, L. C. & Kress, J. W. Practical cone-beam algorithm. Journal of the Optical Society of America A 1, 612, https://doi.org/10.1364/josaa.1.000612 (1984). (PMID: 10.1364/josaa.1.000612)
Wißler, L., Almashraee, M., Monett, D. & Paschke, A. The gold standard in corpus annotation. In IEEE GSC (2014).
Konishi, K. et al. Reducing manual operation time to obtain a segmentation learning model for volume electron microscopy using stepwise deep learning with manual correction. Microscopy 70, 526–535, https://doi.org/10.1093/jmicro/dfab025 (2021). (PMID: 10.1093/jmicro/dfab02534259875)
Gonda, F. et al. VICE: Visual identification and correction of neural circuit errors. Computer Graphics Forum 40, 447–458, https://doi.org/10.1111/cgf.14320 (2021). (PMID: 10.1111/cgf.14320)
Deng, J. et al. Scalable multi-label annotation. In Proceedings of the 32nd annual ACM conference on Human factors in computing systems - CHI 14, https://doi.org/10.1145/2556288.2557011 (ACM Press, 2014).
Romanowski, A., Łuczak, P. & Grudzień, K. X-ray imaging analysis of silo flow parameters based on trace particles using targeted crowdsourcing. Sensors 19, 3317, https://doi.org/10.3390/s19153317 (2019). (PMID: 10.3390/s19153317313577136695825)
Chen, C. et al. Using crowdsourcing for scientific analysis of industrial tomographic images. ACM Trans. Intell. Syst. Technol. 7, https://doi.org/10.1145/2897370 (2016).
Vondrick, C., Ramanan, D. & Patterson, D. Efficiently scaling up video annotation with crowdsourced marketplaces. In Computer Vision – ECCV 2010, 610–623, https://doi.org/10.1007/978-3-642-15561-1_44 (Springer Berlin Heidelberg, 2010).
Pieper, S., Halle, M. & Kikinis, R. 3D Slicer. In 2nd IEEE Int. Symp. on Biomedical Imaging: Nano to Macro (IEEE Cat No. 04EX821), 1, 632–635 s, https://doi.org/10.1109/ISBI.2004.1398617 (2004).
Fedorov, A. et al. 3d slicer as an image computing platform for the quantitative imaging network. Magnetic Resonance Imaging 30, 1323–1341, https://doi.org/10.1016/j.mri.2012.05.001 (2012). (PMID: 10.1016/j.mri.2012.05.001227706903466397)
Kikinis, R., Pieper, S. D. & Vosburgh, K. G. 3d slicer: A platform for subject-specific image analysis, visualization, and clinical support. In Jolesz, F. A. (ed.) Intraoperative Imaging and Image-Guided Therapy, 277–289, https://doi.org/10.1007/978-1-4614-7657-3_19 (Springer New York, 2014).
Dach, C., Held, C., Palmisano, R., Wittenberg, T. & Friedl, S. Evaluation of input modalities for the interactive image segmentation of fluorescent micrographs. In Biomed Tech, 56, S1 (2011).
Gonzalez, R. C. & Woods, R. E. Digital Image Processing (3rd Edition) (Prentice-Hall, Inc., USA, 2006).
Tanno, R., Saeedi, A., Sankaranarayanan, S., Alexander, D. C. & Silberman, N. Learning from noisy labels by regularized estimation of annotator confusion. In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 11236–11245, https://doi.org/10.1109/CVPR.2019.01150 (2019).
Vădineanu, C., Pelt, D. M., Dzyubachyk, O. & Batenburg, K. J. An analysis of the impact of annotation errors on the accuracy of deep learning for cell segmentation. In Konukoglu, E. et al. (eds.) Proceedings of The 5th International Conference on Medical Imaging with Deep Learning, vol. 172 of Proceedings of Machine Learning Research, 1251–1267 (PMLR, 2022).
Entry Date(s):
Date Created: 20240624 Date Completed: 20240624 Latest Revision: 20240627
Update Code:
20250114
PubMed Central ID:
PMC11196272
DOI:
10.1038/s41597-024-03347-4
PMID:
38914545
Database:
MEDLINE
Weitere Informationen
The Me 163 was a Second World War fighter airplane and is currently displayed in the Deutsches Museum in Munich, Germany. A complete computed tomography (CT) scan was obtained using a large scale industrial CT scanner to gain insights into its history, design, and state of preservation. The CT data enables visual examination of the airplane's structural details across multiple scales, from the entire fuselage to individual sprockets and rivets. However, further processing requires instance segmentation of the CT data-set. Currently, there are no adequate computer-assisted tools for automated or semi-automated segmentation of such large scale CT airplane data. As a first step, an interactive data annotation process has been established. So far, seven 512 × 512 × 512 voxel sub-volumes of the Me 163 airplane have been annotated, which can potentially be used for various applications in digital heritage, non-destructive testing, or machine learning. This work describes the data acquisition process, outlines the interactive segmentation and post-processing, and discusses the challenges associated with interpreting and handling the annotated data.
(© 2024. The Author(s).)