The DOI matches its Crossref record; 40 of the 40 references it cites were found in a registry or an index.
MRF-Based Deformable Registration and Ventilation Estimation of Lung CT
Abstract
Deformable image registration is an important tool in medical image analysis. In the case of lung computed tomography (CT) registration there are three major challenges: large motion of small features, sliding motions between organs, and changing image contrast due to compression. Recently, Markov random field (MRF)-based discrete optimization strategies have been proposed to overcome problems involved with continuous optimization for registration, in particular its susceptibility to local minima. However, to date the simplifications made to obtain tractable computational complexity reduced the registration accuracy. We address these challenges and preserve the potentially higher quality of discrete approaches with three novel contributions. First, we use an image-derived minimum spanning tree as a simplified graph structure, which copes well with the complex sliding motion and allows us to find the global optimum very efficiently. Second, a stochastic sampling approach for the similarity cost between images is introduced within a symmetric, diffeomorphic B-spline transformation model with diffusion regularization. The complexity is reduced by orders of magnitude and enables the minimization of much larger label spaces. In addition to the geometric transform labels, hyper-labels are introduced, which represent local intensity variations in this task, and allow for the direct estimation of lung ventilation. We validate the improvements in accuracy and performance on exhale-inhale CT volume pairs using a large number of expert landmarks.
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A Stochastic Approximation Method
Robbins, H., & Monro, S. (1951) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177729586 · Cited by 9,643
Ashburner, J., & Friston, K. J. (2005) · NeuroImage · doi:10.1016/j.neuroimage.2005.02.018 · Cited by 7,507
Fast approximate energy minimization via graph cuts
Boykov, Y., Veksler, O., & Zabih, R. (2001) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.969114 · Cited by 6,988
AVANTS, B., EPSTEIN, C., GROSSMAN, M., & GEE, J. (2008) · Medical Image Analysis · doi:10.1016/j.media.2007.06.004 · Cited by 5,669
Nonrigid registration using free-form deformations: application to breast mr images
Rueckert, D., Sonoda, L. I., Hayes, C., Hill, D. L. G., Leach, M. O., & Hawkes, D. J. (1999) · IEEE Transactions on Medical Imaging · doi:10.1109/42.796284 · Cited by 5,265
An experimental comparison of min-cut/max- flow algorithms for energy minimization in vision
Boykov, Y., & Kolmogorov, V. (2004) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2004.60 · Cited by 4,577
Shortest Connection Networks And Some Generalizations
Prim, R. C. (1957) · Bell System Technical Journal · doi:10.1002/j.1538-7305.1957.tb01515.x · Cited by 4,565
What energy functions can be minimized via graph cuts?
Kolmogorov, V., & Zabih, R. (2004) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2004.1262177 · Cited by 3,127
Pictorial Structures for Object Recognition
Felzenszwalb, P. F., & Huttenlocher, D. P. (2005) · International Journal of Computer Vision · doi:10.1023/b:visi.0000042934.15159.49 · Cited by 2,180
Efficient Belief Propagation for Early Vision
Felzenszwalb, P. F., & Huttenlocher, D. P. (2006) · International Journal of Computer Vision · doi:10.1007/s11263-006-7899-4 · Cited by 1,345
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TransMorph: Transformer for unsupervised medical image registration
Chen, J., Frey, E. C., He, Y., Segars, W. P., Li, Y., & Du, Y. (2022) · Medical Image Analysis · doi:10.1016/j.media.2022.102615 · Cited by 804
Automatic Multi-Organ Segmentation on Abdominal CT With Dense V-Networks
Gibson, E., Giganti, F., Hu, Y., Bonmati, E., Bandula, S., Gurusamy, K., Davidson, B., Pereira, S. P., Clarkson, M. J., & Barratt, D. C. (2018) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2018.2806309 · Cited by 697
Deep learning in medical image registration: a review
Fu, Y., Lei, Y., Wang, T., Curran, W. J., Liu, T., & Yang, X. (2020) · Physics in Medicine & Biology · doi:10.1088/1361-6560/ab843e · Cited by 661
Unsupervised Learning of Probabilistic Diffeomorphic Registration for Images and Surfaces
Dalca, A. V., Balakrishnan, G., Guttag, J., & Sabuncu, M. R. (2019) · Medical Image Analysis · doi:10.1016/j.media.2019.07.006 · Cited by 467
Evaluation of algorithms for Multi-Modality Whole Heart Segmentation: An open-access grand challenge
Zhuang, X., Li, L., Payer, C., Štern, D., Urschler, M., Heinrich, M. P., Oster, J., Wang, C., Smedby, Ö., Bian, C., et al. (2019) · Medical Image Analysis · doi:10.1016/j.media.2019.101537 · Cited by 361
Metadata
- Authors
- Heinrich, H. P., Jenkinson, M., Brady, M., & Schnabel, J. A.
- Venue
- IEEE Transactions on Medical Imaging
- Published
- 2013-07
- Volume
- 32
- Issue
- 7
- Pages
- 1239-1248
- ISSN
- 0278-0062 (ISSN-L)
- 1558-254X
- OpenAlex
- W2016974693
- Cited by
- 311
- References
- 40
- FWCI
- 21.19
- Type
- journal article
- Language
- en
- Open access
- closed
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- Heinrich, H. P., Jenkinson, M., Brady, M., & Schnabel, J. A. (2013). MRF-Based Deformable Registration and Ventilation Estimation of Lung CT. IEEE Transactions on Medical Imaging. https://doi.org/10.1109/tmi.2013.2246577
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