The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
Patch-based segmentation using expert priors: Application to hippocampus and ventricle segmentation
Open accessAbstract
Quantitative magnetic resonance analysis often requires accurate, robust, and reliable automatic extraction of anatomical structures. Recently, template-warping methods incorporating a label fusion strategy have demonstrated high accuracy in segmenting cerebral structures. In this study, we propose a novel patch-based method using expert manual segmentations as priors to achieve this task. Inspired by recent work in image denoising, the proposed nonlocal patch-based label fusion produces accurate and robust segmentation. Validation with two different datasets is presented. In our experiments, the hippocampi of 80 healthy subjects and the lateral ventricles of 80 patients with Alzheimer's disease were segmented. The influence on segmentation accuracy of different parameters such as patch size and number of training subjects was also studied. A comparison with an appearance-based method and a template-based method was also carried out. The highest median kappa index values obtained with the proposed method were 0.884 for hippocampus segmentation and 0.959 for lateral ventricle segmentation.
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Cites
Image quality assessment: from error visibility to structural similarity
Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004) · IEEE Transactions on Image Processing · doi:10.1109/tip.2003.819861 · Cited by 57,892
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A Non-Local Algorithm for Image Denoising
Buades, A., Coll, B., & Morel, J.-M. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.38 · Cited by 7,616
A nonparametric method for automatic correction of intensity nonuniformity in MRI data
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Region Filling and Object Removal by Exemplar-Based Image Inpainting
Criminisi, A., Perez, P., & Toyama, K. (2004) · IEEE Transactions on Image Processing · doi:10.1109/tip.2004.833105 · Cited by 3,258 · cited 2×
“…patch-based methods are currently the focus of attention of the computer vision community in various domains such as texture synthesis (Efros and Freeman, 2001), in-painting (Criminisi et al., 2004), restoration (Buades et al., 2005), and single-frame super resolution (Protter et al., 2009).”
BackgroundMethodHippocampal Volume Reduction in Major Depression
Bremner, J. D., Narayan, M., Anderson, E. R., Staib, L. H., Miller, H. L., & Charney, D. S. (2000) · American Journal of Psychiatry · doi:10.1176/ajp.157.1.115 · Cited by 1,670
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Rates of hippocampal atrophy correlate with change in clinical status in aging and AD
Jack, C. R., Petersen, R. C., Xu, Y., O’Brien, P. C., Smith, G. E., Ivnik, R. J., Boeve, B. F., Tangalos, E. G., & Kokmen, E. (2000) · Neurology · doi:10.1212/wnl.55.4.484 · Cited by 937
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Self-citationTanskanen, P., Veijola, J. M., Piippo, U. K., Haapea, M., Miettunen, J. A., Pyhtinen, J., Bullmore, E. T., Jones, P. B., & Isohanni, M. K. (2005) · Schizophrenia Research · doi:10.1016/j.schres.2004.09.022 · Cited by 75 · cited 1×
“Moreover, HC dysfunction is involved in a variety of diseases, including AD (Jack et al., 2000), posttraumatic stress disorder (Bremner et al., 1995), major depression (Bremner et al., 2000), schizophrenia (Buss et al., 2007; Tanskanen et al., 2005), and epilepsy (Bernasconi et al., 2003).”
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The multimodal brain tumor image segmentation benchmark (BRATS)
Menze, B. H., Jakab, A., Bauer, S., Kalpathy-Cramer, J., Farahani, K., Kirby, J., Burren, Y., Porz, N., Slotboom, J., Wiest, R., et al. (2015) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2014.2377694 · Cited by 6,781
Self-citationEvaluation of prostate segmentation algorithms for mri: The promise12 challenge
Litjens, G., Toth, R., van de Ven, W., Hoeks, C., Kerkstra, S., van Ginneken, B., Vincent, G., Guillard, G., Birbeck, N., Zhang, J., et al. (2014) · Medical Image Analysis · doi:10.1016/j.media.2013.12.002 · Cited by 857
Brain atrophy in Alzheimer’s Disease and aging
Pini, L., Pievani, M., Bocchetta, M., Altomare, D., Bosco, P., Cavedo, E., Galluzzi, S., Marizzoni, M., & Frisoni, G. B. (2016) · Ageing Research Reviews · doi:10.1016/j.arr.2016.01.002 · Cited by 836
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
Brosch, T., Tang, L. Y. W., Yoo, Y., Li, D. K. B., Traboulsee, A., & Tam, R. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2528821 · Cited by 460
Metadata
- Authors
- Coupé, P., Manjón, J. V., Fonov, V., Pruessner, J., Robles, M., & Collins, D. L.
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2011-01
- Volume
- 54
- Issue
- 2
- Pages
- 940-954
- ISSN
- 1053-8119 (ISSN-L)
- OpenAlex
- W2010587020
- Cited by
- 692
- References
- 10
- FWCI
- 32.43
- Type
- journal article
- Language
- en
- Open access
- green
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- Coupé, P., Manjón, J. V., Fonov, V., Pruessner, J., Robles, M., & Collins, D. L. (2011). Patch-based segmentation using expert priors: Application to hippocampus and ventricle segmentation. NeuroImage. https://doi.org/10.1016/j.neuroimage.2010.09.018
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