The DOI matches its Crossref record; 27 of the 27 references it cites were found in a registry or an index.
Multi-Atlas-Based Segmentation With Local Decision Fusion—Application to Cardiac and Aortic Segmentation in CT Scans
Open accessAbstract
A novel atlas-based segmentation approach based on the combination of multiple registrations is presented. Multiple atlases are registered to a target image. To obtain a segmentation of the target, labels of the atlas images are propagated to it. The propagated labels are combined by spatially varying decision fusion weights. These weights are derived from local assessment of the registration success. Furthermore, an atlas selection procedure is proposed that is equivalent to sequential forward selection from statistical pattern recognition theory. The proposed method is compared to three existing atlas-based segmentation approaches, namely 1) single atlas-based segmentation, 2) average-shape atlas-based segmentation, and 3) multi-atlas-based segmentation with averaging as decision fusion. These methods were tested on the segmentation of the heart and the aorta in computed tomography scans of the thorax. The results show that the proposed method outperforms other methods and yields results very close to those of an independent human observer. Moreover, the additional atlas selection step led to a faster segmentation at a comparable performance.
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Automatic anatomical brain MRI segmentation combining label propagation and decision fusion
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Multi-Atlas Segmentation with Joint Label Fusion
Wang, H., Suh, J. W., Das, S. R., Pluta, J. B., Craige, C., & Yushkevich, P. A. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.143 · Cited by 796
Multi-atlas segmentation of biomedical images: A survey
Iglesias, J. E., & Sabuncu, M. R. (2015) · Medical Image Analysis · doi:10.1016/j.media.2015.06.012 · Cited by 667
Multi-Scale Deep Reinforcement Learning for Real-Time 3D-Landmark Detection in CT Scans
Ghesu, F.-C., Georgescu, B., Zheng, Y., Grbic, S., Maier, A., Hornegger, J., & Comaniciu, D. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2017.2782687 · Cited by 310
Doshi, J., Erus, G., Ou, Y., Resnick, S. M., Gur, R. C., Gur, R. E., Satterthwaite, T. D., Furth, S., & Davatzikos, C. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2015.11.073 · Cited by 309
“, 2014), or local similarity based weighting in label fusion (Artaechevarria et al., 2009; Isgum et al., 2009; Khan et al., 2011; Wang et al., 2013a) were proposed to address these challenges, by selecting, either locally or globally, warps most similar to the target image.”
Automatic multiorgan segmentation in thorax CT images using U‐net‐ GAN
Dong, X., Lei, Y., Wang, T., Thomas, M., Tang, L., Curran, W. J., Liu, T., & Yang, X. (2019) · Medical Physics · doi:10.1002/mp.13458 · Cited by 275
Metadata
- Authors
- Isgum, I., Staring, M., Rutten, A., Prokop, M., Viergever, M. A., & van Ginneken, B.
- Venue
- IEEE Transactions on Medical Imaging
- Published
- 2009-07
- Volume
- 28
- Issue
- 7
- Pages
- 1000-1010
- ISSN
- 0278-0062 (ISSN-L)
- 1558-254X
- OpenAlex
- W2162140684
- Cited by
- 361
- References
- 27
- FWCI
- 26.02
- Type
- journal article
- Language
- en
- Open access
- green
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- Isgum, I., Staring, M., Rutten, A., Prokop, M., Viergever, M. A., & van Ginneken, B. (2009). Multi-Atlas-Based Segmentation With Local Decision Fusion—Application to Cardiac and Aortic Segmentation in CT Scans. IEEE Transactions on Medical Imaging. https://doi.org/10.1109/tmi.2008.2011480
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